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The natural property of rock material, whether impact occurs, is the key influencing factor of the occurrence of rock burst disaster. To accurately assess rock burst proneness, this study focuses on typical sandstone as the research object. Uniaxial cyclic loading and unloading tests were conducted to measure the elastic strain energy accumulated in sandstone under different stress levels and a relationship between elastic strain energy and stress level was established. The results show that: (1) The peak stress under cyclic loading and unloading conditions is slightly lower than the uniaxial compressive strength. With an increase in the number of cycles, the internal damage of sandstone continues to accumulate, and the mechanical properties such as compressive strength continue to deteriorate; (2) With an increase in stress, the input strain energy, elastic strain energy, and dissipated strain energy also increase; (3) When the stress is low, the increase in elastic strain energy is large and shows a steady growth; with an increase in stress, the increase of elastic strain energy decreases; (4) The square of stress at any time has a good linear relationship with elastic strain energy. According to the relationship obtained from the test, the elastic strain energy at the peak stress time can be obtained; (5) A new criterion for assessing rock burst proneness is proposed: residual energy release rate index WT, which characterizes the energy release per unit time when the rock burst occurs. The intervals for evaluating the rock burst proneness of the residual energy release rate index WT are as follows: WT 2, indicating strong rock burst proneness; and (6) The rationality of the proposed residual energy release rate index WT is verified by the multi-index method and the multi-sample method, and the proposed residual energy release rate index is used to determine the rock burst proneness of 10 kinds of rock samples. The evaluation accuracy is shown to be high, and it can reflect the actual rock burst proneness

Abstract Most studies evaluated the energy release rate of cortical bone macrostructure under Mode I, Mode II, and mixed Mode I-II loading conditions. However, testing the macrostructural energy release rate requires an initial crack and recording the applied load and the corresponding crack length in real-time, which may introduce measurement errors and differences with the actual fracture scenarios. To further understand how the energy release rate contributed to the cortical bone fracture characteristics, this study predicted the microstructural energy release rate of cortical bone and then investigated its age-related varitions. The microstructural energy release rate of femoral cortical bone in rats from different ages was back-calculated by fitting the experimental and simulated load–displacement curves under compression load. The trends in the microstructural energy release rate were revealed, and the underlying reasons for the age-related changes were investigated by integrating the discussion on the cortical bone mechanical parameters at various levels obtained from the previous experiment. The predicted microstructural energy release rate of femoral cortical bone in the rats from 1, 3, 5, 7, 9, 11, and 15 months of age were in the range of 0.08–0.12, 0.12–0.14, 0.15–0.19, 0.25–0.28, 0.23–0.25, 0.19–0.22, and 0.13–0.16 N/mm, respectively. The statistical analyses showed the significant differences in the microstructural energy release rate at different ages. The results indicated an increasing trend followed by a decrease from 1 to 15 months of age, and the correlations between microstructural energy release rate and age were significant. The age-related variations in the microstructural energy release rate may be linked to the changes in the microarchitecture, and the fracture load is influenced by the micro-level mechanical parameters. Notably, the age-related trends in microarchitecture and energy release rate were similar. These findings were valuable for understanding the mechanism underlying the weakening mechanical properties of cortical bone microstructure with age from an energy perspective.

Abstract In this study, a discriminative model of fracture expansion through a composite reservoir interface was established. During fracture transformation, the model, based on the theory of energy release rate at the fracture tip, helps clarify the critical conditions and assess the law of hydraulic fracture expansion through coal composite reservoir interfaces. This model considers shear and tensile processes during fracturing at the coal–rock interface and the effects of interface material property differences on fracture extension. In addition, fracture extension pattern differentiation is achieved by comparing the hydraulic fracture tip penetration and bending energy release rates (G p /G d ) with the ratio of reservoir fracture and interface fracture energies (ΓR/ΓF). The accuracy of this discriminant criterion was verified by comparing the predicted results of the discriminant model with fracturing test results under identical conditions. The effects of various factors on G p /G d and hydraulic fracture penetration extension were investigated by using the sand-mudstone composite reservoir of the Shibox Formation in the Linxing Block, China, as the geological background. The results indicate that for certain values of ΓR/ΓF, G p /G d increases logarithmically with an increase in water pressure in the wellbore. Moreover, the difference in ground stress and elastic modulus between the layers exponentially increases with increasing Poisson’s ratio difference and the angle between fracture and interface and decreases logarithmically with increasing fracture height. These results indicate that hydraulic fractures are likely to penetrate from reservoirs with a high elastic modulus and Poisson’s ratio into reservoirs with a low elastic modulus and Poisson’s ratio. Furthermore, hydraulic fractures are likely to penetrate formations with large differences in pinch angle and ground stress. Under specific geological conditions, high water pressure in the wellbore and small seam height are favourable for fracture penetration.

Accurate predictions of HRR will improve preparedness and response strategies, enhance safety, and minimise damage in tunnel fires. In this study, a deep learning prediction model for HRR under multimodal data fusion is proposed. A multimodal dataset is first established to obtain flame images and flue gas time series data through model-scale tunnel fire experiments. During the model training process, ResNet18 was used to extract features from the flame image, and 2CLSTM was employed to understand the time series of the flame image features and flue gas features to establish the correlation with the HRR. It was evaluated that the error analyses of the measured and predicted values of the validation set yielded R2 greater than 0.85, with errors and standard deviations less than 4 kW. And the model predicted better in the flame growth and decay phases. However, there is some deviation in the predictions near the peak HRR. To address this issue, the Bagging algorithm was introduced to optimise the model. The results show that the ResNet18_2CLSTM model with Bagging reduces the RMSE by 20.47 % and increases the R2 by 4.64 % compared to the original model, and the accuracy is greatly improved.

This paper investigated the mutual interaction between two extra-thin line fires under different wind speeds of 0.4, 0.6, 0.8, 1.0 and 1.2 m/s by using a small-scale wind tunnel. The experiments involved the spacing distances of 5, 10, 15, 20, 25, 30, 35 and 40 cm, and the heat release rates of 19.4, 25.7 and 32.7 kW, respectively. The results show that the flame merging probability increase, as the wind speed or the heat release rate increases. Compared with the two rectangular or square fires, the flame height and flame length will be smaller at the same heat release rate for the extra-thin line fires. Meanwhile, the line fires will decrease the flame merging, making the downstream flame temperature smaller than that of the upstream at the relatively larger wind speed or heat release rate. The interesting finding is that, with the increase of the heat release rate, the flame inclination angle will increase in the fully merging stage, which is opposite to that of square fires. Correspondingly, the flame merging model is built on the influences of the buoyancy force and inertial force of the horizontal wind, which can give the good explanation on this behavior.

In order to investigate and solve the reality that sesame seeds were rich in calcium, but rarely used as calcium supplement food. In this study, two kinds of black and white sesame seeds were used as raw materials, which were processed in four ways: Steaming, microwave, baking and ultra-high pressure (UHP). Afterwards, the basic components were analyzed, the release rate of calcium ions from sesame seeds was determined using in vitro simulated digestion, and a mouse model of phytic acid inhibition of calcium absorption was constructed to evaluate the effects of different processing methods on the calcium bioavailability of sesame seeds. The results showed that steaming processing and roasting at 180 ℃ resulted in a significant decrease (P0.05) in phytic acid content of black and white sesame seeds, and UHP processing resulted in a significant increase (P0.05), baking at 120 ℃ also resulted in a significant increase in calcium release from white sesame seeds (P

Recently, commercial flexible silicon(Si) solar cells have been available for charging batteries and electronic devices. In this research, we present a methodology for determining the critical energy release rate at the EVA/Si cell interface of a flexible silicon solar cell, which can also be applied to other interfaces in solar cells. The outline of procedure is as follows: • Conduct a peeling test at the EVA/Si cell interface of the solar cell sample • Perform a tensile test on the upper layer of the solar cell sample • Execute IC Peel softwareInputs: Peeling force between EVA and Si layer, Young's modulus (E), Yield stress (σy), and Yield strain (εy) of the upper layerOutput: critical energy release rate (Gc) between EVA and the silicon (Si) layer

Introduction. Wet granulation technology is a process of directed particle aggregation of powder materials to obtain required properties of tablet masses and, as a consequence, to achieve satisfactory characteristics of tablets. In this addition, as a result of wet granulation technology, if special excipients are used, it becomes possible to control the rate and kinetics of release of active pharmaceutical substances from tablets to achieve the desired therapeutic effect.Aim. To study the effect of matrix-forming components included in the composition of mixtures for granulation on the rate of release of sodium 4,4'-(propanediamido)dibenzoate from tablets.Materials and methods. The original substance sodium 4,4'-(propanediamido)dibenzoate, as well as a number of excipients, which included polymers used for prolonged-release dosage forms, lubricant – sodium stearyl fumarate, as well as pore-forming agents – PVP and MCC, were the objects of the study. The key parameters of tablets and dissolution kinetics were studied in accordance with the requirements of State Pharmacopoeia of the Russian Federation XIV edition and Pharmacopeia of the Eurasian Economic Union.Result and discussion. Prolonged release was achieved for all tablets, but more than 90 % of the substance was released after 12 hours in tablets containing ethylcellulose as a matrix-forming polymer. The release of APS from tablets of this formulation was the most prolonged.Сonclusion. The effect of matrix-forming components included in the composition of mixtures for granulation on the rate of release of 4,4'-(propanediamido)sodium dibenzoate from tablets has been studied. The most uniform and complete release of ASF from tablets in which the matrix-forming polymer is ethylcellulose in the amount of 27.7 %.

This study investigated the relationship between fracture toughness (Kc) and energy release rate (Gc) through fracture morphology analysis, emphasizing the critical role of fractal dimensions in accurately characterizing fracture surfaces. Traditional linear elastic fracture mechanics (LEFM) models relate Gc to Kc by combining energy principles with the nominal area of the fracture surface. However, real materials often exhibit plasticity, and their fracture surfaces are not regular planes. To address these issues, this research applied fractal theory and introduced the concept of ubiquitiform surface area to refine the calculation of fracture surfaces, leading to more accurate estimates of Gc and Kc. The method was validated through standard compact tensile specimen tests on a nickel-based superalloy at 550 °C. Additionally, the analysis of fractal dimension differences and dispersion in various fracture regions provides a novel perspective for evaluating the fracture toughness of materials.

Accurate measurement of fire source heat release rate is crucial for comprehensively understanding the fire evolution process. However, the widely used oxygen consumption method requires expensive equipment, incurring high costs. This study proposes a comprehensive framework based on machine learning to predict fire source heat release rate using temperature as input. Firstly, fire scenarios with different parameters in ISO9705 room were simulated using FDS software to obtain temperature at various locations, establishing a fire database. Then, two recursive feature elimination algorithms based on the Lasso and the Random Forest (RF) models were employed separately for feature selection, resulting in two different low-dimensional feature subsets and a control group. Finally, different feature subsets were input to analyse and compare the prediction performance on the heat release rate of three typical algorithms: linear regression (LR), K-nearest neighbor (KNN), and lightGBM. Results indicate that the LightGBM model trained with the feature subset selected by the recursive feature elimination algorithm based on the Random Forest model exhibits the best predictive performance, with root mean square error (RMSE) and mean absolute error (MAE) of 23.89 kW and 15.49 kW respectively, and a coefficient of determination (R2) of 0.9916. This comprehensive framework based on machine learning demonstrates excellent predictive performance and is cost-effective, providing a new and effective approach for predicting fire source heat release rate.

The ocean is the largest carbon pool on earth and plays an important role in the global carbon cycle. Marine organisms contribute over half of the global carbon fixed by photosynthesis every year. Therefore, much attention has been paid to the role of the ocean in the carbon cycle and carbon sequestration. Macroalgae are an important component of coastal ecosystems, with strong and highly efficient carbon fixation capacity. The net primary productivity (NPP) of macroalgae was higher than that of phytoplankton and other primary producers in coastal waters. Parts of the photosynthates are released into the sea in the form of dissolved organic carbon (DOC) and particulate organic carbon (POC) during algae growth. Dissolved organic carbon and POC have attracted widespread attention because they can play important roles in carbon cycling and carbon sequestration in coastal ecosystems. However, the organic carbon release rate of macroalgae, its relationship with primary productivity, and the regulatory factors require further study. The growth of algae and the organic carbon release are influenced by temperature, light intensity, photoperiod, and other factors. However, organic carbon release rates among algae species might be quite different. Therefore, studying the regulatory effects of different factors on organic carbon release by algae and organic carbon release rate distribution has important implications for quantitatively evaluating the organic carbon release ability of algae and its contribution to the coastal ecosystem carbon cycle.In recent years, Sargassum horneri is the most common wild macroalgae in the Yellow Sea. Its ecological role in the coastal ecosystem has attracted widespread attention. Few studies have focused on organic carbon release by S. horneri. This study conducted an orthogonal experiment to measure DOC and POC release rates of S. horneri at different temperatures (5, 15, and 25 ℃), light intensity [86, 172, and 258 μmol/(m2·s)] and photoperiod (L: D=6 h: 18 h, L: D=12 h: 12 h, L: D=24 h: 0 h, L means light time, D means dark time) to study the organic carbon release rate of S. horneri, its relationship with NPP, and the main regulatory factors. The release rates of DOC were 0.653–4.785 mg/(g·h) and the release rates of POC were 0.066–0.322 mg/(g·h). There is an order of magnitude difference between DOC and POC. This indicated that DOC was the main form of organic carbon released by S. horneri. The highest DOC release rate [4.785 mg/(g·h)] occurred at 25 ℃, 172 μmol/(m2·s) light intensity, and L: D = 6 h: 18 h. The highest POC release rate [0.322 mg/(g·h)] occurred at 25 ℃, 258 μmol/(m2·s) light intensity, and L: D = 24 h: 0 h. The organic carbon release ability of S. horneri is consistent with previous studies of other macroalgae.This study found that changes in temperature and light conditions significantly affect the ability of S. horneri to release organic carbon. Temperature was the main factor affecting the release rate of DOC; the DOC release rate increases with increasing temperature. The DOC release rate initially increases, then decreases with increasing light intensity; it increases with a decrease in the photoperiod. Light intensity was the main factor influencing the POC release rate; increasing light intensity and photoperiod accelerated the POC release rate. However, the POC release rate slightly decreased when the light intensity increased from 172 to 258 μmol/(m2·s). There was a greater POC release rate under extreme temperature stress (5 and 25 ℃).This study showed that a proportion of DOC and POC in NPP were unstable in macroalgae. The range of DOC/NPP was 4%–130%. POC/NPP was relatively concentrated to approximately 0.4%–5.9%. The NPP was significantly lower in the experimental group with a high ratio of organic carbon compared with other experimental groups. The DOC release rate negatively correlated with NPP, but the negative correlation was poor in the low temperature groups. There was no significant correlation between the POC release rate and NPP. POC/NPP was relatively stable [approximately (1±0.6)% when NPP>9 mg C/(g·h)]. The growth pressure of S. horneri was high when NPP

BACKGROUND: Critical energy release rate is a global fracture parameter that could be measured during the failing process, and its value may change under different failure modes even in the same structure. OBJECTIVE: To propose an approach to predict the critical energy release rate in the femoral cortical bone structure under different failure modes. METHODS: Three-point bending and axial compression experiments and the corresponding fracture simulations were performed on the rat femoral cortical bone structures. Different critical energy release rates were repeatedly assigned to the models to perform fracture simulation, and the predicted loaddisplacement curves in each simulation were compared with the experimental data to back-calculate the critical energy release rate. The successful fit was that the differences in the fracture parameters between the predicted and experimental results were less than 5%. RESULTS AND CONCLUSION: (1) The results showed that the cortical bone structure occurred tensile open failure under three-point bending load, and the predicted critical energy release rate was 0.16 N/mm. (2) The same cortical bone structure occurred shear open failure under axial compression load, and the predicted critical energy release rate was 0.12 N/mm, which indicates that the critical energy release rate of the same cortical bone structure under different failure modes was different. (3) A comprehensive analysis from the perspectives of material mechanical properties and damage mechanism was conducted to reveal the reasons for the differences in the critical energy release rate in the cortical bone structure under different failure modes, which provided a theoretical basis for the measurement of the energy release rate and the accurate fracture simulation. [ABSTRACT FROM AUTHOR]