Coalburst disasters induced by thrust fault slip frequently take place in underground coal mines and often lead to severe roadway damage and personnel casualties. However, the triggering mechanism of coalburst remains unclear. Combined with field monitoring, physical analog simulation and numerical simulation, this study identifies the segmented slip behavior of thrust faults induced by mining activities, reveals the formation mechanism of stress concentration in coal bodies between mining panels and the fault, and analyzes the generation sources of dynamic stress disturbance. Furthermore, laboratory coupled static-dynamic loading tests are carried out on hollow coal specimens simulating underground roadways, and the progressive damage and failure characteristics of coal are investigated. The main conclusions are as follows: (1) As mining advances toward the thrust fault, the fault exhibits obvious segmented slip: the upper fault segment slips anticlockwise while the lower segment slips clockwise. (2) The clamping effect generated by segmented fault slip is the primary factor inducing high stress concentration in the coal between the working face and the thrust fault. (3) Coal bodies bearing high static stress are highly susceptible to unstable failure and subsequent coalburst under superimposed dynamic stress loading. (4) Tensile stress waves reflected at roadway surfaces break surrounding rock, triggering coalburst accompanied by sidewall spalling and floor heave. (5) Segmented fault slip and its resultant clamping effect are two core factors responsible for thrust fault-slip coalburst. This paper puts forward a new viewpoint that segmented fault slip acts as the fundamental trigger of thrust fault-slip coalburst, which provides an in-depth theoretical basis for revealing the occurrence mechanism of such coalburst disasters.

B-box (BBX) transcription factors are emerging as pivotal regulators of environmental adaptation and developmental plasticity in plants. These proteins act at the intersection of light, hormonal and stress signalling networks to modulate key processes, including photomorphogenesis, circadian rhythm regulation, abiotic and biotic stress responses, anthocyanin biosynthesis and flowering time control. Recent studies in various model species and crops have revealed that BBX proteins can function as both activators and repressors of transcription, often by directly interacting with key regulators such as HY5, PRR9/7 and MYC2. These interactions enable BBX factors to fine-tune gene expression in response to dynamic environmental conditions. Functionally, BBX proteins orchestrate light-responsive development, enhance tolerance to drought, salinity, and pathogens via hormonal and reactive oxygen species (ROS)-mediated pathways, and regulate secondary metabolism linked to pigment accumulation. Their roles in reproductive development, particularly in controlling flowering time and vegetative-reproductive phase transitions, position them as promising targets for crop improvement. Despite growing insight, key knowledge gaps remain. The mechanistic basis of BBX duality, their post-translational regulation and their integration within broader transcriptional and chromatin networks are still poorly understood. Additionally, BBX-mediated signalling remains understudied in monocots, wild relatives and under complex field conditions. This review summarizes the latest mechanistic and evolutionary insights into BBX transcription factors, emphasizing their functional diversity, context-dependent regulation, and applications in precision breeding. By highlighting both translational applications and unresolved challenges, we propose future directions for using BBX proteins to design of climate-resilient, high-performance crops.

Cold stress poses a significant threat to viticulture, particularly under the increasing pressures of climate change. In this study, we identified VaMIEL1, a RING-type E3 ubiquitin ligase from Vitis amurensis, as a negative regulator of cold tolerance. Under normal temperature conditions, VaMIEL1 facilitates the ubiquitination and subsequent proteasomal degradation of the cold-responsive transcription factor VaMYB4a, thereby attenuating its regulatory role in the CBF-COR signaling cascade. However, under cold stress, VaMIEL1 expression is downregulated, leading to the stabilization of VaMYB4a and the activation of CBF-COR signaling. Through a combination of biochemical assays and functional analysis in Arabidopsis thaliana and grapevine calli, we demonstrate that VaMIEL1 overexpression reduces cold tolerance, as evidenced by increased oxidative stress, excessive reactive oxygen species (ROS) accumulation, and downregulated expression of cold-responsive genes. Conversely, silencing of VaMIEL1 enhances cold tolerance by stabilizing VaMYB4a and boosting antioxidant defenses. These findings uncover a previously unrecognized regulatory mechanism by which VaMIEL1 modulates cold tolerance through transcriptional and oxidative stress pathways, offering potential targets for the development of climate-resilient grapevine cultivars and other crops.

The analysis of historical coal mine safety events and the accurate identification of disaster factors are essential for effective mine safety management. Based on the KeyBERT network model, conducts a coupling analysis of six typical coal mine disaster cases in China between 2013 and 2023: gas explosions, water disasters, fires, roof collapses, coal dust incidents, and rockbursts. It utilizes the 24Model (a theoretical model of accident causation) to systematically analyze the mechanisms of each causative factor. The research reveals that causative factors of coal mine accidents can be classified into three categories: geological factors representing hazardous conditions of materials, serving as prerequisites for disaster occurrences; behavioral and managerial factors reflecting unsafe human behaviors, crucial as trigger conditions for disasters. Moreover, deeply explored the disaster-causing characteristics and action mechanisms of key geological factors such as faults, folds, goafs and overburden structures, and divided behavioral factors into two levels: psychological and executive. It was found that psychological factors play a leading role in accident induction. When psychological factors are superimposed on problems at the executive level, major safety hazards will be formed, seriously threatening coal mine safety production. Based on these findings, we have developed a dual-prevention mechanism integrating hidden danger investigation with safety risk classification control, and proposed an innovative “3LA” coal mine disaster management system, revealing that the inevitability of mine disasters stems from simultaneous failures at three management levels.

It has been well-known that the incompressible Smoothed Particle Hydrodynamics (ISPH) is a powerful method for simulating violent wave-structure interactions (WSIs) concerned in marine engineering. However it is time consuming, primarily due to the need of solving pressure Poisson’s equation (PPE) involved in this method. In our previous publications, we are first to propose a hybrid approach embedding the graph neural network (GNN) into ISPH method to form the hybrid ISPH_GNN method for simulating free-surface problems, where the GNN is employed to replace solving the PPE. We demonstrated that the computational time for evaluating the pressure using GNN can be of one order less than that spent by directly solving PPE to achieve similar level of accuracy. More importantly, we also demonstrated in our previous publications that the GNN trained only on data for wave-only (referring to no structure or obstacles in wave fields) cases can be satisfactorily applied to the cases for wave-floater interactions. However, what we have not previously studied is if the GNN trained only by using wave-only cases can be used for simulating violent WSIs. One of the original contributions of this paper is to answer this question. In addition, transfer learning has been proved to be a machine learning (ML) technique that can significantly enhance efficiency and improve the performance in other fields but has not been explored in the hybrid ISPH_GNN method. Another original contribution of this paper is to explore the potential of integrating transfer learning with the ISPH_GNN for simulating violent WSIs. Specifically, we will demonstrate that the GNN trained by using data from sloshing and dam-breaking cases without any structure (termed as wave-only data in this paper) can be employed to simulate more complex cases, such as water entry of an object, wave impact on a trapezoidal structure and wave interaction with an oscillating wave surge converter, all of which involve violent WSIs. We will also demonstrate that the transfer learning technique with use of a small volume of additional data has a potential in enhancing the prediction accuracy of the ISPH_GNN. Furthermore, we will show that the ISPH_GNN significantly reduces computational time for pressure evaluation in violent WSI cases, even with a more significant reduction compared to wave-floater interaction cases studied in our previous work. These highlight the strong potential of the ISPH_GNN for broad applications in marine engineering, opening a novel route to employ ML without need of generating data for very complex cases of violent WSIs.

Coal is the main energy source in China, but coal mining is a high-risk industry, making the prevention and control of coal mining hazards an important topic. Constrained by the complexity and unpredictability of underground spaces, current research on coal mining disaster prevention and control technologies mainly focuses on the characteristics of overlying strata and the laws of mine pressure, resulting in significant deficiencies in accuracy. Given this, a data-driven pressure prediction method is proposed, which uses deep learning models to learn the patterns in existing data and generate the required predictions. This approach avoids the challenges of accurately extracting rock mass physical and mechanical parameters and geological structure modeling, thereby improving the accuracy of disaster prevention and control. The stage of working face pressure exertion is a period prone to disasters during coal mining. To achieve accurate prediction of working face pressure, the task is divided into three steps: the first step is to predict support resistance data ahead of the working face, the second step is to classify the pressure labels of coordinate units, and the third step is to predict the characteristic parameters of pressure exertion. Deep learning models were designed and trained separately for each of the three steps: For the first step, a deep Spatiotemporal sequence model was selected, and the trained model achieved a mean absolute error of 4.65 kN in prediction. For the second step, an image segmentation-based classification model was chosen, with the trained model reaching a classification accuracy of 97.77%. For the third step, a fusion model consisting of three LSTM (Long Short-Term Memory) networks was designed. The trained model achieved a mean absolute error of 0.17 for the dynamic pressure coefficient, a maximum resistance error of 810.93 kN during the pressure period, an error of 9.96 cycles for the pressure duration, and a classification accuracy of 92.35% for the pressure type. Simulating the actual situation of application scenarios, the input data for the second and third steps were set as the output data from the previous step, and the model was evaluated. The model achieved a mean absolute error of 1035.21 kN for the prediction of support resistance and classification accuracy of 82.90% for the pressure labels of coordinate units. In the simulated scenario, there were 9922 instances of pressure exertion, and the model predicted 10,336 instances, with 9046 of them matching the actual instances. The prediction of characteristic parameters was evaluated for 4946 instances of pressure exertion, which included three complete pressure exertion cycles. The mean absolute error for the dynamic pressure coefficient was 0.21, the maximum resistance error during the pressure period was 1218.31 kN, the error for the duration of the pressure cycle was 11.03 cycles, and the classification accuracy for the pressure exertion type was 91.75%.

Genetic transformation in horticultural crops is being reshaped by the emergence of nontissue culture technologies that bypass entrenched barriers of genotype dependence, regeneration inefficiency, and sterile culture requirements. This review surveys recent in planta methods, including regenerative activity-dependent in Planta injection delivery (RAPID), cut-dip-budding (CDB), virus-based delivery, nanoparticle-mediated transformation, and Agrobacterium rhizogenes-induced regeneration, and evaluates their operational versatility across species. We further examine their integration with developmental regulators (BABY BOOM [BBM], WUSCHEL [WUS]), visual markers (RUBY), and CRISPR/Cas systems to enhance transformation efficiency and precision. Case studies across fruit, vegetable, and ornamental crops illustrate broad applicability and growing technical maturity. Despite these advances, unresolved challenges in biosafety, reproducibility, and regulatory alignment remain. We advocate a new transformation paradigm that is rapid, genotype-independent, and environmentally compatible, enabling scalable and more accessible broadly applicable crop improvement in horticultural biotechnology.

This paper summarises the work conducted within the 1st FOWT (Floating Offshore Wind Turbine) Comparative Study organised by the EPSRC (UK) ‘Extreme loading on FOWTs under complex environmental conditions’ and ‘Collaborative computational project on wave structure interaction (CCP-WSI)’ projects. The hydrodynamic response of a FOWT support structure is simulated with a range of numerical models based on potential theory, Morison equation, Navier-Stokes solvers and hybrid methods coupling different flow solvers. A series of load cases including the static equilibrium tests, free decay tests, operational and extreme focused wave cases are considered for the UMaine VolturnUS-S semi-submersible platform, and the results from 17 contributions are analysed and compared with each other and against the experimental data from a 1:70 scale model test performed in the COAST Laboratory Ocean Basin at the University of Plymouth. It is shown that most numerical models can predict similar results for the heave response, but significant discrepancies exist in the prediction of the surge and pitch responses as well as the mooring line loads. For the extreme focused wave case, while both Navier–Stokes and potential flow base models tend to produce larger errors in terms of the root mean squared error than the operational focused wave case, the Navier-Stokes based models generally perform better. Given the fact that variations in the solutions (sometimes large) also present in the results based the same or similar numerical models, e.g., OpenFOAM, the study highlights uncertainties in setting up a numerical model for complex wave structure interaction simulations such as those involving a FOWT and therefore the importance of proper code validation and verification studies.

Cold stress severely impacts the quality and yield of grapevine (Vitis L.). In this study, we extend our previous work to elucidate the role and regulatory mechanisms of Vitis amurensis MYB transcription factor 4a (VaMYB4a) in grapevine's response to cold stress. Our results identified VaMYB4a as a key positive regulator of cold stress. We demonstrated that VaMYB4a undergoes phosphorylation by V. amurensis calcineurin B-like (CBL) proteins-interacting protein kinase 18 (VaCIPK18) under cold stress, a process that activates VaMYB4a transcriptional activity. Using chromatin immunoprecipitation sequencing (ChIP-seq). We performed a comprehensive genomic search to identify downstream components that interact with VaMYB4a, leading to the discovery of a basic helix-loop-helix transcription factor, V. amurensis phytochrome-interacting factor 3 (VaPIF3). VaMYB4a attenuated the transcriptional activity of VaPIF3 through a phosphorylation-dependent interaction under cold conditions. Furthermore, VaPIF3, which interacts with and inhibits V. amurensis C-repeat binding factor 4 (VaCBF4, a known positive regulator of cold stress), has its activity attenuated by VaMYB4a, which mediates the modulation of this pathway. Notably, VaMYB4a also interacted with and promoted the expression of VaCBF4 in a phosphorylation-dependent manner. Our study shows that VaMYB4a positively modulates cold tolerance in plants by simultaneously downregulating VaPIF3 and upregulating VaCBF4. These findings provide a nuanced understanding of the transcriptional response in grapevine under cold stress and contribute to the broader field of plant stress physiology.

Cold stress profoundly affects the growth, development, and productivity of horticultural crops. Among the diverse strategies plants employ to mitigate the adverse effects of cold stress, flavonoids have emerged as pivotal components in enhancing plant resilience. This review was written to systematically highlight the critical role of flavonoids in plant cold tolerance, aiming to address the increasing need for sustainable horticultural practices under climate stress. We provide a comprehensive overview of the role of flavonoids in the cold tolerance of horticultural crops, emphasizing their biosynthesis pathways, molecular mechanisms, and regulatory aspects under cold stress conditions. We discuss how flavonoids act as antioxidants, scavenging reactive oxygen species (ROS) generated during cold stress, and how they regulate gene expression by modulating stress-responsive genes and pathways. Additionally, we explore the application of flavonoids in enhancing cold tolerance through genetic engineering and breeding strategies, offering insights into practical interventions for improving crop resilience. Despite significant advances, a research gap remains in understanding the precise molecular mechanisms by which specific flavonoids confer cold resistance, especially across different crop species. By addressing current knowledge gaps, proposing future research directions and highlighting implications for sustainable horticulture, we aim to advance strategies to enhance cold tolerance in horticultural crops.

Addressing the insufficient pressure relief in the deep coal mass and the deterioration of the surrounding rock bearing structure due to increased borehole diameter in conventional borehole pressure relief techniques, a static expansive fracturing method for pressure relief in coal seam boreholes is proposed. A critical stress model for static expansive fracturing in coal seam boreholes is established through theoretical analysis, and numerical simulations are conducted to compare and analyze the differences between this method and conventional borehole pressure relief techniques in terms of roadway pressure relief effect and surrounding rock stability control. The results indicate that using a 150 mm borehole with a 75 MPa expansive stress achieves a comparable state of full pressure relief in the rib coal as a 250 mm borehole, while increasing the pressure relief range in the deep coal mass by a factor of approximately 3.6. Although conventional borehole pressure relief methods can enhance pressure relief by increasing the borehole diameter, they significantly expand the plastic zone in the shallow surrounding rock, aggravating surrounding rock deformation. In contrast, the static expansive fracturing method for pressure relief in coal seam boreholes actively directs fractures in the deep high-stress coal mass, promoting the expansion and interconnection of its plastic zone to form a deep structural weakening zone, while markedly reducing the disturbance and damage to the shallow surrounding rock. This method breaks through the limitations of passive borehole for pressure relief, offering dual advantages of efficient deep pressure relief and shallow surrounding rock stability protection, and providing a new approach for rockburst prevention.

The surrounding rock of the repeated mining roadway is severely deformed and cannot be reused,and the repeated mining roadway has obvious overlapping extension features during the service period.In order to solve the above problems,this study takes the 13092 roadway of Guanjiaya Coal Mine as the research background,and adopts on-site measurement,numerical simulation,and theoretical analysis methods to investigate the overlapping extension features and control measures of repeated mining roadway deformation.The analysis of deformation features of repeated mining roadways shows the following points.① Under a single mining disturbance,the deformation of repeated mining roadways exhibits zoning and asymmetric failure features,which can be divided into rapid deformation zone,strong deformation zone,and slow deformation zone.The crack damage mainly occurs in the coal wall and coal pillar walls,with less damage to the roof and floor,manifested as significant fragmentation and inward movement of the two sides of the roadway.Severe deformation occurs at the intersection of the coal wall and roof,as well as the coal pillar and floor.(2)The secondary mining roadway expands and overlaps on the basis of the primary damage,making the asymmetric damage more significant and forming a butterfly shaped plastic failure zone in the surrounding rock of the roadway.③ The key time for controlling the surrounding rock of the repeated mining roadway is the first mining stage.The key area is the coal pillar side of the roadway in the strong deformation zone and the slow deformation zone.By analyzing the butterfly deformation features and failure zoning rules of mining roadways,a multi-level coupling control technology for repeated mining roadways is proposed.Shallow low pressure-deep high pressure grouting is used to improve the support force of coal pillars.The anchor cables are used to reinforce and improve the support force of support bodies,achieving coupling control.The comparative analysis of deformation before and after reinforcement has verified that multi-level coupling control meets the requirements of roadway reuse.

As a mesh-free approach, the incompressible Smoothed Particle Hydrodynamics (ISPH) method has been often used for simulating wave-structure interaction problems. In the conventional ISPH method, the pressure-projection phase of solving the pressure Poisson's equation (PPE) is the most time-consuming. In recent years, the machine learning (ML) techniques has gradually shown their potential in accelerating the computational fluid dynamics. In this paper, the graph neural network (GNN) supported ISPH method (ISPH_GNN), in which the GNN replaces solving the PPE for the fluid pressure in the conventional ISPH, is adopted for numerical simulations of wave-floater interactions. To the best of the authors' knowledge, this is the first work to study the wave-floater interactions by using GNN supported ISPH method. More importantly, this paper demonstrates that the GNN trained only on data for simpler wave-only cases (i.e. no structure in the wave fields) can be satisfactorily applied to the cases for wave-floater interactions. More specifically, the paper will show this by using the ISPH_GNN with such trained GNN model to simulate various different cases, including the decay tests of a box, a floating box subjected to a wave, the interaction between wave and a moored floating breakwater and the violent green water impact on a floating structure. In most of the cases, the numerical results are validated by comparing with experimental data. Agreement between them is surprisingly satisfactory, being as good as those obtained by the conventional ISPH. The paper will also show that the ISPH_GNN requires much less computational time (97 times less for the cases concerned) than the conventional ISPH for estimating pressure involved in wave-floater interactions. This reveals a great potential that one can train the GNN using the datasets for simpler cases and then use the ISPH_GNN to simulate wave-floater interaction problems.

Horticultural crops suffer massive production losses due to abiotic stress, which is a key limiting factor worldwide. The ability of these crops to withstand such stress has been linked to melatonin, a biomolecule with significant roles in both physiological and molecular defense responses. Melatonin is pivotal in enhancing the resilience of horticultural crops to abiotic stress, making it a critical component in their survival strategies. The application of exogenous melatonin improves abiotic stress tolerance by preserving membrane integrity, maintaining redox equilibrium, scavenging reactive oxygen species effectively, activating antioxidant defense mechanisms, and elevating gene expression related to stress responses. Furthermore, the integrated management of melatonin with other phytohormones demonstrates its potential relevance in addressing various stresses across a wide range of horticultural crops. Therefore, it is crucial to elucidate the physiological and molecular processes involving melatonin in abiotic stress in these crops. Here, we discuss current studies on the use of melatonin in horticultural crops in response to abiotic stresses, and explores future research directions and potential applications to enhance the productivity and abiotic stress tolerance of horticultural crops.

Cold stress can limit the growth and development of grapevines, which can ultimately reduce productivity. However, the mechanisms by which grapevines respond to cold stress are not yet fully understood. Here, we characterized an APETALA2/ethylene response factor (AP2/ERF) which was shown to be a target gene of our previously identified VaMYB4a from Amur grape. We further investigated the molecular interactions between VaMYB4a and VaERF054-like transcription factors in grapes and their role in cold stress tolerance. Our results demonstrated that VaMYB4a directly binds to and activates the VaERF054-like gene promoter, leading to its enhanced expression. Moreover, we also explored the influence of ethylene precursors and inhibitors on VaERF054-like expression and grape cold tolerance. Our findings indicate that VaERF054-like contribute to cold tolerance in grapes through modulation of the ethylene pathway and the CBF signal pathway. Overexpression of VaERF054-like in Vitis vinifera 'Chardonnay' calli and transgenic grape lines resulted in increased freezing stress tolerance, confirming its role in the cold stress response. We further confirmed the interaction between VaMYB4a and VaERF054-like in vivo and in vitro. The co-transformation of VaMYB4a and VaERF054-like in grape calli demonstrates a synergistic interaction, enhancing the cold tolerance through a regulatory feedback mechanism. Our finding provides new insights into grape cold tolerance mechanisms, potentially contributing to the development of cold-resistant grape varieties.

The existing prevention and control of rockbursts in mining mainly focus on the production phase, making it challenging to fundamentally curb rockburst disasters. Based on the concept of lifecycle management, the lifecycle of coal mines can be divided into four stages: exploration, construction, production, and closure. "Source" prevention and control measures are implemented during different stages of the mine to address rockbursts. During the exploration stage, the emphasis is on assessing the rockburst proneness and predicting the risk of rockbursts in the newly developed coal seams. In the construction stage, the focus is on identifying the dynamic tendencies and evaluating the risk of rockbursts considering all minable coal seams, as well as the roof and floor strata. This involves conducting a rockburst identification for the mine, establishing a sound prevention mechanism, improving management systems, determining mine capacity, and implementing rockburst prevention designs. In the production stage, rockburst prevention and control measures are implemented in three stages: pre-mining, during mining, and post-mining. During each stage, specific measures are undertaken to mitigate the risks associated with rockbursts. During the closure stage, safety assessments are conducted regarding the recovery of coal pillars to prevent rockbursts. Special prevention measures are developed based on the assessment results. By implementing these measures throughout the entire lifecycle of the mine, from exploration to closure, it becomes possible to address the issue of rockbursts comprehensively and effectively. This approach ensures that preventive actions are taken at the early stages of mine development and continues to manage and control rockburst risks during the operational phases, ultimately enhancing safety and reducing the impact of rockburst disasters.

This work uses machine learning to produce synthetic data of wave energy converters from time-expensive 3D simulations based on computational fluid dynamics models. The simulations to analyse the response of these systems to incoming waves are lengthy and computationally expensive to obtain. Here, we explore the use of a beta-VAE and a Principal Components-based adversarial autoencoder for generating new synthetic data. The compression plus the generation of synthetic data introduces an exceptionally fast surrogate model of the original simulation and delivers more samples of either dynamic viscosity and velocity fields, enlarging the design space. The new generated synthetic samples can have a speed up from 5 to 6 orders of magnitude. The new design space can be used to improve the prediction of dynamic viscosity given the velocity fields. The generative model has the potential to capture the transition and the new physical phenomena under extreme initial conditions.

Semi-submersible offshore platforms are often produced in the deep sea far away from land, which results in high operating costs and difficult safety maintenance due to the harsh and complex working conditions. Adopting a neural network model for online accurate prediction of mooring tension of semi-submersible offshore platforms can adjust the ballast according to the advance of mooring tension change to keep the platform in a relatively smooth movement amplitude and improve production efficiency. In addition, it can also prevent catastrophic accidents caused by anchor chain breakage due to excessive mooring tension. However, the measured mooring tension of semi-submersible offshore platforms is characterized by complex nonlinear non-stationarity, which leads to insufficient prediction accuracy of a single prediction model. This paper develops a novel online prediction model of mooring tension for semi-submersible offshore platforms based on empirical modal decomposition (EMD), convolutional neural network (CNN), and bidirectional long and short-term memory neural network (BiLSTM). The proposed model is validated using the measured data of the first semi-submersible offshore platform in the South China Sea during operation. The experimental results show that the proposed model outperforms other comparative models currently mainstream for mooring tension prediction. In addition, the proposed method can also be used to predict complex nonlinear data in the field of ship and ocean engineering.

The incompressible Smoothed Particle Hydrodynamics (ISPH) is a popular Lagrangian Particle method. In the conventional ISPH method for simulating free-surface flows, the pressure-projection phase, which solves the pressure Poisson's equation (PPE), is the most time-consuming. In this paper, we propose a novel hybrid method by combining the graph neural network (GNN) with the ISPH for modelling the free-surface flows. In the new hybrid method, the graph neural network (GNN) is employed to replace solving the PPE for pressure in the conventional ISPH. To the best of knowledge of the authors, this is the first attempt to combine the GNN with ISPH model in a Lagrangian formulation. The performance of the hybrid method will be evaluated by comparing its results with experimental data, analytical solution or numerical results from other methods for three benchmark test cases: dam breaking, sloshing wave and solitary wave propagation. In addition, the potential of generalization of the hybrid method will be studied by applying it with the GNN model trained on data for relatively simple cases to simulate more complex cases. It will be demonstrated that the hybrid method does not only give satisfactory results, but also shows good potential of generalization. In addition, the new method will be demonstrated to require the computation time which can be 80 times less than the conventional ISPH for estimating pressure for cases with a large number of particles that is usually needed in the practical free-surface flows simulation using ISPH.

Effects of strain rate on specific fracture energy and micro-fracture surface properties of rock specimen were investigated at strain rates of similar to 60-115.0 s(-1). To achieve this, rock breakage was conducted under dynamic uniaxial compression using the split Hopkinson's pressure bar. The fracture surface area of fine fragments (<8*10(-3)m) were measured using gas absorption method while the fracture surface area of large fragments (>8*10(-3)m) were measured using close-range photogrammetry. Results reveal that fracture surface area and specific fracture energy generally increase with strain rate during rock breakage under dynamic uniaxial compression. Similarly, the total fracture surface area of broken fragments also increased with dissipated energy. Results from scanning electron microscopy showed that fracture surface roughness and macro-crack abundance also increased with strain rate. Following energy requirement for rock breakage into size specifications for engineering applications, strain rate provides a reliable parameter for estimating specific fracture energy and fracture surface area at the forementioned strain rates. Accordingly, optimizing rock breakage processes using strain rate could reduce ore loss and en-ergy wastage associated with production of unwanted fracture surfaces during rock breakage.