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The development of conductive coatings has significant implications for microelectronics and electrochemistry. However, conductive coatings may exhibit different electrochemical properties when prepared on different substrate materials. This research explores the comparative performance of graphene, graphene oxide (GO), and silver nanoparticle (Ag NP) composites as conductive coatings on diverse substrate materials, including polydimethylsiloxane (PDMS), polymethyl methacrylate (PMMA), and glass. The study employed various preparation methods, such as mixing conductive materials with substrate materials and preparing copolymer composite materials. The conductive coating approach was found to be the most straightforward and convenient, with broader development prospects and fewer restrictive conditions. The results indicate that the distinct surface characteristics of the substrate materials influence the conductive properties of coating materials. Consequently, results show that graphene exhibits the highest conductivity on all three substrates, while GO is more conductive than Ag NPs on PMMA and PDMS but less conductive than Ag NPs on glass. That offers valuable insights into the selection of substrate materials and coating materials for the preparation of conductive materials.

Rock bursts are among the most severe and unpredictable hazards encountered in deep rock engineering, posing substantial threats to both construction safety and project progress. This study provides a comprehensive investigation into how moisture infiltration influences the propensity for rock bursts, aiming to establish new theoretical foundations and practical methods for their prevention. Through the analysis of meticulous laboratory mechanical experiments and sophisticated numerical simulations, we analyzed the variations in the physical and mechanical properties of rocks under different moisture conditions, with a particular focus on strength, brittleness, and energy release characteristics. The findings reveal that moisture infiltration significantly diminishes rock strength and reduces the likelihood of brittle fractures, thereby effectively mitigating the risk of rock bursts. Additionally, further research indicates that in high-moisture environments, the marked reduction in rock burst tendency is attributed to increased rock toughness and the suppression of crack propagation. This study advocates for the implementation of moisture control measures as a pre-treatment strategy for deep rock masses. This innovative approach presents a viable and effective solution to enhance engineering safety and improve construction efficiency, offering a practical method for managing rock burst risks in challenging environments.

The use of glass fiber-reinforced polymer (GFRP) composites in fluid transport systems can effectively reduce corrosion damage caused by corrosive media. However, collisions between solid particles and the surfaces of ball valve flow passages can cause erosion damage and lead to safety issues. The two-phase flow and erosion characteristics of ball valves manufactured from resin-based fiber-reinforced composite materials were studied under different openings and particle sizes using the CFD-DPM method. The results indicate that both smaller and larger relative openings are prone to erosion damage at the thin edges of the valve ball. As the relative opening increases, the average erosion amount in the flow passage first increases and then decreases. The maximum average erosion amount is 0.0051 kg/m2·s when the relative opening is Cv = 40. At Cv = 40, erosion damage in the flow channel mainly occurs at the bottom of the inlet flow channel and the valve seat position. With increasing particle size, both the average and maximum erosion amounts in the flow channel increase. Larger particle sizes in the inlet flow channel significantly raise the erosion rate nearby, while at other locations, larger particle sizes mainly increase the erosion rate in the same area. During the use of GFRP valves, it is important to avoid introducing large-sized particles into the medium. Keeping the valve’s relative opening greater than 40 and using more erosion-resistant materials for the valve seat can effectively reduce the erosion of the composite ball valve and extend its service life.

The proton exchange membrane electrolysis cell (PEMEC) has attracted considerable attention for large-scale and efficient hydrogen production because of its high current density, high hydrogen purity and fast dynamic response. Flow field distributions and water and thermal management characteristics of a PEMEC are vital for electrolytic cell structure and the determination of operating condition. A three-dimensional, non-isothermal, electrochemical model of a PEMEC was established in this manuscript. The flow field distribution and water and thermal management of the PEMEC are discussed. The corresponding results showed that the pressure of the flow channel decreased diagonally from the inlet to the outlet, and the pressure and velocity distribution exhibited a downward opening shape of a parabola. At the same inlet flow rate, when the voltage was 1.6 V, the oxygen generation rate was 15.74 mol/(cm2·s), and when the voltage was 2.2 V, the oxygen generation rate was 332.05 mol/(cm2·s); due to the change in the oxygen production rate, the pressure difference at 2.2 V was 2.5 times than that at 1.6 V. When the stoichiometric number was less than two, the average temperature of the catalyst layer (CL) decreased rapidly with the increase in the water flow rate. When the voltage decreased to 2.1 V, the current density came to the highest value when the stoichiometric number was 0.7, then the current density decreased with an increase in the stoichiometric number. When stoichiometric numbers were higher than five, the surface temperature and current density remained basically stable with the increase in the water flow rate, and the water and thermal management and electrolysis characteristics performed better. The research results could optimize the water supply of electrolysis cells. According to the velocity distribution law of the flow field, the water and thermal management performance of the PEMEC could be estimated, further promoting safety and reliability.

Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent optimisation algorithms and deep learning techniques and collects 287 sets of real rockburst data to form a sample database, in which six quantitative indicators are selected as feature parameters. In order to validate the effectiveness of the constructed eight machine learning prediction models, the study selected Accuracy, Precision, Recall and F1 Score to evaluate the prediction performance of each model. The results show that the NGO-CNN-BiGRU-Attention model has the best prediction performance, with an accuracy of 0.98. Subsequently, engineering validation of the model is carried out using eight sets of real rockburst data from Daxiangling Tunnel, and the results show that the model has a strong generalisation ability and can satisfy the relevant engineering applications. In addition, this paper also uses SHAP technology to quantify the impact of different factors on the rockburst intensity level and found that the elastic strain energy index and stress ratio have the greatest impact on the rockburst intensity level.

Exploring the spatiotemporal dynamic patterns of multi-channel electroencephalography (EEG) is crucial for interpreting dementia and related cognitive decline. Spatiotemporal patterns of EEG can be described through microstate analysis, which provides a discrete approximation of the continuous electric field patterns generated by the brain cortex. Here, we propose a novel microstate spatiotemporal dynamic indicator, termed the microstate sequence non-randomness index (MSNRI). The essence of the method lies in initially generating a sequence of microstate transition patterns through state space compression of EEG data using microstate analysis. Following this, we assess the non-randomness of these microstate patterns using information-based similarity analysis. The results suggest that this MSNRI metric is a potential marker for distinguishing between health control (HC) and frontotemporal dementia (FTD) (HC vs. FTD: 6.958 vs. 5.756, p < 0.01), as well as between HC and populations with Alzheimer’s disease (AD) (HC vs. AD: 6.958 vs. 5.462, p < 0.001). Healthy individuals exhibit more complex macroscopic structures and non-random spatiotemporal patterns of microstates, whereas dementia disorders lead to more random spatiotemporal patterns. Additionally, we extend the proposed method by integrating the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method to explore spatiotemporal dynamic patterns of microstates at specific frequency scales. Moreover, we assessed the effectiveness of this innovative method in predicting cognitive scores. The results demonstrate that the incorporation of CEEMD-enhanced microstate dynamic indicators significantly improved the prediction accuracy of Mini-Mental State Examination (MMSE) scores (R2 = 0.940). The CEEMD-enhanced MSNRI method not only aids in the exploration of large-scale neural changes in populations with dementia but also offers a robust tool for characterizing the dynamics of EEG microstate transitions and their impact on cognitive function.

This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a path and speed decoupled planning method for non-split vehicle trajectory planning on structured roads. Firstly, the path planning layer adopts the improved artificial potential field method. The obstacle-repulsive potential field, gravitational potential field, and fitting method of the traditional artificial potential field are improved. Secondly, the speed planning aspect is performed in the Frenet coordinate system. Speed planning is accomplished based on S-T graph construction and solving convex optimization problems. Finally, simulation and experimental verification are performed. The results show that the method proposed in this paper can significantly improve the safety and comfortable riding of the vehicle.

Understanding the actual soil salt ion content and salt type is one of the important prerequisites for determining the nature of saline soils and their development and utilization in drylands. Desert–sierozem soils are widely distributed in the plains between the northern piedmont of the Tianshan Mountains and the Gurbantunggut Desert in Xinjiang, Northwest China, which contain abundant calcium sulfate (gypsum) and calcium carbonate and are high-quality arable land resources for agriculture. These soils have been extensively reclaimed for farmland in recent decades. In this study, 10 plots of desert–sierozem soils with different tillage years in the Qitai Oasis plain area in the northern piedmont of Tianshan Mountain were studied. Three soil profiles were excavated in each sample plot with a depth of 1.2 m. A total of 30 farmland profiles were dug up and sampling was conducted according to the soil occurrence layers of the farmland. All the soil samples were predominantly sandy in texture, with more than 92% of the soil particles distributed between 0.05 and 0.25 mm, and less than 8% between 0.002 and 0.05 mm. Conventional water extraction (CWE) and intensified water extraction (IWE) were adopted to extract the water-soluble salt ions content of K+, Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3− and CO32−, and this study compared the extraction results using two different methods with varying extraction strengths. Additionally, the calcium sulfate and calcium carbonate contents were obtained using the chemical extraction and mineral assay methods, respectively. Differences in the salts and salt types in desert–sierozem soils with different tillage years were analyzed, and the effect of calcium sulfate on the inorganic carbon sinks in plowed desert–sierozem soil was demonstrated. The findings indicated that the standard conventional extraction procedure involving a 5:1 water–soil ratio and 3 min of shaking was inadequate for accurately measuring the soil salt content in high calcium sulfate content desert–sierozem soils. With the IWE method, by increasing the water–soil ratio, prolonging the shaking time, and grinding the particles finer, the amount of water-soluble salts extracted using the IWE method was 5.83 times higher than that of the conventional method. Among them, the average content of IWE-Mg2+ increased by 123.41 mg kg−1 compared to CWE-Mg2+, the average content of IWE-Ca2+ was 3.82 times higher than CWE-Ca2+, with an average increase of 2931.59 mg kg−1, and the IWE-SO42− content was 5.96 times higher than CWE-SO42−, with an average increase of 8658.95 mg kg−1. A markedly negative correlation (p < 0.01) was observed between calcium carbonate and calcium sulfate in desert–sierozem soils after tillage. The calcium sulfate content consistently decreased and calcium carbonate increased with an increasing number of tillage years. The pedogenic calcium carbonate content in desert–sierozem soils increased by an average of 10.86 g kg−1 after more than 40 years of cultivation, and the transfer of Ca2+ from calcium sulfate to calcium carbonate was identified as the cause of the increase in the soil inorganic carbon sink. Overall, a new method for extracting water-soluble salts was utilized for aridisols, which commonly consist of complex salt types. The results elucidate the changes in salinity within plowed desert–sierozem soils as well as the impact of soil salt on soil inorganic carbon sinks. Based on our research, desert–sierozem soils are well suited for agricultural farming because they contain high levels of sulfate, which is one of the important inorganic nutrients essential for crops, and it also provides a source of calcium for increasing inorganic carbon sinks. This study serves as a reference and scientific basis for soil carbon cycles in arid zones.

To study the stress distribution characteristics of surrounding rock and the spalling mechanism of deep hard rock tunnels with different arch heights, the complex variable function and angle-preserving transformation method in elasticity theory were applied to the analytic solution of tangential stress distribution of arch tunnels during stress adjustment. In addition, true triaxial tests were conducted on granite cube specimens (100 mm × 100 mm × 100 mm) containing holes with three arch heights (including the 25 mm semi-circular arch, 16.7 mm three-centered arch, 12.5 mm three-centered arch) to simulate the spalling process under different initial ground stresses. The stress distribution solution and experimental results show that the initial failure stress of arch holes is 0.39–0.48 times the uniaxial compressive strength (UCS) of the rock. The initial failure location occurs at the arch foot, where tangential stress maximizes. When the lateral pressure coefficient is in the range of 0.38–0.50, the tangential stress is 3.2–3.5 times the UCS. The rock debris of the hole wall are in thin flake shapes. Symmetrical V-shaped or curved failure zones occurred on hole sidewalls. The stress distribution resolution of the surrounding rock of tunnels with different arch heights shows that with the increasing burial depth, the bearing performance of the semi-circular arch tunnel is optimal. In addition, the maximum tangential stress increases as the height of the arch decreases or the lateral stress increases, making it easier for the initial failure to occur at the foot of the arch.

Biological toxicity testing plays an essential role in identifying the possible negative effects induced by substances such as organic pollutants or heavy metals. As an alternative to conventional methods of toxicity detection, paper-based analytical device (PAD) offers advantages in terms of convenience, quick results, environmental friendliness, and cost-effectiveness. However, detecting the toxicity of both organic pollutants and heavy metals is challenging for a PAD. Here, we show the evaluation of biotoxicity testing for chlorophenols (pentachlorophenol, 2,4-dichlorophenol, and 4-chlorophenol) and heavy metals (Cu2+, Zn2+, and Pb2+) by a resazurin-integrated PAD. The results were achieved by observing the colourimetric response of bacteria (Enterococcus faecalis and Escherichia coli) to resazurin reduction on the PAD. The toxicity responses of E. faecalis-PAD and E. coli-PAD to chlorophenols and heavy metals can be read within 10 min and 40 min, respectively. Compared to the traditional growth inhibition experiments for toxicity measuring which takes at least 3 h, the resazurin-integrated PAD can recognize toxicity differences between studied chlorophenols and between studied heavy metals within 40 min.

Engineering rock structures containing joints and fissures are frequently subjected to discontinuous periodic disturbances of varying amplitudes. To attain the quantitative relationship between the crack angle and the mechanical and deformation properties of rock under complex stress paths, uniaxial cyclic loading and unloading tests with increasing stress gradients were conducted on sandstone specimens containing a single crack of different angles. Our results showed that the bearing capacity of the sandstone increased as the crack angle increased. The irreversible strain and elastic moduli of the rock presented a sudden increase when entering the next cycle of the stepped loading. However, the entire loading process can be divided into three stages according to their respective trends. These three stages correspond to the three stages of rock deformation, i.e., the pore crack compaction stage, the elastic deformation to the stable micro-elastic crack development stage, and the unstable crack development stage. In addition, the crack angle of sandstone showed a negative correlation with the irreversible strain, but a positive correlation with the elastic modulus. With the increasing crack angle, the failure mode of the rock changed from the tensile-shear failure to the shear failure, and then to the interlayer dislocation failure.

Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate ground settlement prediction has great significance in ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from the Singapore Circle Line rail traffic project containing seven defining parameters to create a database for predicting ground settlement. These parameters are the tunnel depth (H), the tunnel advance rate (AR), the EPB earth pressure (EP), the mean SPTN value from the soil crown to the surface (Sm), the mean water content of the soil layer (MC), the mean modulus of elasticity of the soil layer (E), and the grout pressure used for injecting grout into the tail void (GP). Three hybrid models consisting of random forest (RF) and three types of meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), and Grasshopper Optimization Algorithm (GOA), were developed to predict ground settlement. Furthermore, the mean absolute error (MAE), the mean absolute percentage error (MAPE), the coefficient of determination (R2) and the root mean square error (RMSE) were used to assess predictive performance of the constructed models for predicting ground settlement. The evaluation results demonstrated that the GOA-RF with a population size of 10 has achieved the most outstanding predictive capability with the indices of MAE (Training set: 2.8224; Test set: 2.3507), MAPE (Training set: 40.5629; Test set: 38.5637), R2 (Training set: 0.9487; Test set: 0.9282), and RMSE (Training set: 4.93; Test set: 3.1576). Finally, the sensitivity analysis results indicated that MC, AR, Sm, and GP have a significant impact on ground settlement prediction based on the GOA-RF model.

The compressive strength, shrinkage, elasticity, and electrical resistivity of the cement-soil pastes (slag, fly ash) of self-healing of cementitious concrete have been studied while adding hydrogels with nano silica (NSi) in this research. Defining the hydraulic and mechanical properties of these materials requires improvement to motivate more uptake for new buildings. Initially, examining the impact of different synthesized hydrogels on cement-soil pastes showed that solid particles in the mixtures highly affected the absorption capacity of NSi, representing the importance of direct interactions between solid particles and hydrogels in a cementitious matrix. All test results were analyzed by use of a hybridized soft computing model such as the adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) for precise studying and the avoidance of few empirical tests or error percentages. Subsequently, the best RMSE of ANFIS is 0.6568 and the best RMSE of SVM is 1.2564; the RMSE of ANFIS-SVM (0.5643) in the test phase is also close to zero, showing a better performance in hypothesizing self-healing soil-cementitious hydrogel materials in mine backfill. The R2 value for ANFIS-SVM is 0.9547, proving that it is a proper model for predicting the study’s goal. Electrical resistivity and compressive strength declined in the cement-soil pastes including hydrogels according to experimental outcomes; it was lowered by the increase of NSi concentration in the hydrogel. There was a decrement in the autogenous shrinkage of cement-soil pastes while adding hydrogel, depending on the NSi concentration in the hydrogels. The findings of this research are pivotal for the internal curing of cementitious materials to define the absorption of hydrogels.

While adding superabsorbent polymer hydrogel particles to fresh concrete admixtures, they act as internal curing agents that absorb and then release large amounts of water and reduce self-desiccation and volumetric shrinkage of cement that finally result in hardened concrete with increased durability and strength. The entrainment of microscopic air bubbles in the concrete paste can substantially improve the resistance of concrete. When the volume and distribution of entrained air are adequately managed, the microstructure is protected from the pressure produced by freezing water. This study addresses the design and application of hydrogel nanoparticles as internal curing agents in concrete, as well as new findings on crucial hydrogel–ion interactions. When mixed into concrete, hydrogel particles produce their stored water to power the curing reaction, resulting in less volumetric shrinkage and cracking and thereby prolonging the service life of concrete. The mechanical and swelling performance qualities of the hydrogel are very sensitive to multivalent cations found naturally in concrete mixes, such as aluminum and calcium. The interactions between hydrogel nanoparticles and alkaline cementitious mixes are described in this study, while emphasizing how the chemical structure and shape of the hydrogel particles regulate swelling behavior and internal curing efficiency to eliminate voids in the admixture. Moreover, in this study, an artificial neural network (ANN) was utilized to precisely and quickly analyze the test results of the compressive strength and durability of concrete. The addition of multivalent cations reduced swelling capacity and changed swelling kinetics, resulting in fast deswelling behavior and the creation of a mechanically stiff shell in certain hydrogel compositions. Notably, when hydrogel particles were added to a mixture, they reduced shrinkage while encouraged the creation of particular inorganic phases within the void area formerly held by the swelled particle.

To investigate the influence of depth on the rockburst of surrounding rock in a circular tunnel, true-triaxial tests at different depths were carried out on cubic granite specimens with a circular through-going hole. A micro camera was used to monitor the rockburst process of the circular hole sidewall in real time. The test results show that the failure process at different depths can be divided into four periods: the calm period, the particle ejection period, the rock fragment exfoliation period, and the rock bursting period. With an increase in depth, the three-dimensional unequal stress state gradually increased; the failure range and the size of rock fragments increased, the initial failure vertical stress linearly increased, and the strength and stability of the surrounding rock were enhanced. Therefore, the support range of surrounding rock should be increased as the depth increased to improve the overall stability of surrounding rock and reduce the failure range.

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.

A new etherified starch, δ-hydroxybutyl starch (δ-HBS), was prepared by utilising 4-chlorobutan-1-ol as the etherifying reagent. The method of Zeisel gas chromatography for the determination of the molar substitution was described. This technique offers a simple and rapid method for quantitative analysis with reproducible results. Meanwhile, the mechanism of the Zeisel reaction was also investigated.

Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).

The purpose of this study is to analyze the dynamic properties of gas hydrate development from a large hydrate simulator through numerical simulation. A mathematical model of heat transfer and entropy production of methane hydrate dissociation by depressurization has been established, and the change behaviors of various heat flows and entropy generations have been evaluated. Simulation results show that most of the heat supplied from outside is assimilated by methane hydrate. The energy loss caused by the fluid production is insignificant in comparison to the heat assimilation of the hydrate reservoir. The entropy generation of gas hydrate can be considered as the entropy flow from the ambient environment to the hydrate particles, and it is favorable from the perspective of efficient hydrate exploitation. On the contrary, the undesirable entropy generations of water, gas and quartz sand are induced by the irreversible heat conduction and thermal convection under notable temperature gradient in the deposit. Although lower production pressure will lead to larger entropy production of the whole system, the irreversible energy loss is always extremely limited when compared with the amount of thermal energy utilized by methane hydrate. The production pressure should be set as low as possible for the purpose of enhancing exploitation efficiency, as the entropy production rate is not sensitive to the energy recovery rate under depressurization.

Solar-to-chemical conversion via photocatalysis is of paramount importance for a sustainable future. Thus, investigating the synergistic effects promoted by light in photocatalytic reactions is crucial. The tandem oxidative coupling of alcohols and amines is an attractive route to synthesize imines. Here, we unravel the performance and underlying reaction pathway in the visible-light-driven tandem oxidative coupling of benzyl alcohol and aniline employing Au/CeO2 nanorods as catalysts. We propose an alternative reaction pathway for this transformation that leads to improved efficiencies relative to individual CeO2 nanorods, in which the localized surface plasmon resonance (LSPR) excitation in Au nanoparticles (NPs) plays an important role. Our data suggests a synergism between the hot electrons and holes generated from the LSPR excitation in Au NPs. While the oxygen vacancies in CeO2 nanorods trap the hot electrons and facilitate their transfer to adsorbed O2 at surface vacancy sites, the hot holes in the Au NPs facilitate the α-H abstraction from the adsorbed benzyl alcohol, evolving into benzaldehyde, which then couples with aniline in the next step to yield the corresponding imine. Finally, cerium-coordinated superoxide species abstract hydrogen from the Au surface, regenerating the catalyst surface.

Microseismic (MS) source location is a fundamental and critical task in mine MS monitoring. The traditional ray tracing-based location method can be easily affected by many factors, such as multi-ray path effects, waveform focusing and defocusing of wavefield propagation, and low picking precision of seismic phase arrival. By contrast, the Gaussian beam reverse-time migration (GBRTM) location method can effectively and correctly model the influences of multi-path effects and wavefield focusing and defocusing in complex 3D media, and it takes advantages of the maximum energy focusing point as the source location with the autocorrelation imaging condition, which drastically reduces the requirements of signal-to-noise ratio (SNR) and picking accuracy of P-wave arrival. The Gaussian beam technique has been successfully applied in locating natural earthquake events and hydraulic fracturing-induced MS events in one-dimensional (1D) or simple two-dimensional (2D) velocity models. The novelty of this study is that we attempted to introduce the GBRTM technique into a mine MS event location application and considered utilizing a high-resolution tomographic 3D velocity model for wavefield back propagation. Firstly, in the synthetic test, the GBRTM location results using the correct 2D velocity model and different homogeneous velocity models are compared to show the importance of velocity model accuracy. Then, it was applied and verified by eight location premeasured blasting events. The synthetic results show that the spectrum characteristics of the recorded blasting waveforms are more complicated than those generated by the ideal Ricker wavelet, which provides a pragmatic way to evaluate the effectiveness and robustness of the MS event location method. The GBRTM location method does not need a highly accurate picking of phase arrival, just a simple detection criterion that the first arrival waveform can meet the windowing requirements of wavefield back propagation, which is beneficial for highly accurate and automatic MS event location. The GBRTM location accuracy using an appropriate 3D velocity model is much higher than that of using a homogeneous or 1D velocity model, emphasizing that a high-resolution velocity model is very critical to the GBRTM location method. The average location error of the GBRTM location method for the eight blasting events is just 17.0 m, which is better than that of the ray tracing method using the same 3D velocity model (26.2 m).

Multiscale entropy (MSE) is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP) dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE for characterizing COP displacement dynamics. We identified five key methodological procedures that varied significantly between studies: (1) data length; (2) frequencies of the COP dynamics analyzed; (3) sampling rate; (4) point matching tolerance and sequence length; and (5) filtering of displacement changes from drifts, fidgets, and shifts. We discuss strengths and limitations of the various approaches employed and supply flowcharts to assist in the decision making process regarding each of these procedures. Our guidelines are intended to more broadly inform the design and analysis of future studies employing MSE for continuous time series, such as COP.

The Split-Hopkinson pressure bar (SHPB) was used to determine the fracture characteristics of a long bar rock specimen with an elliptical cavity under different axial ratios and dip angles. A high speed camera was applied to record the fracturing process of the granite specimen around the cavity. The experimental results showed that the fracture characteristics around the elliptical cavity were closely related to the axial ratio and dip angle. A three-dimensional numerical model was established using LS-DYNA to quantitatively analyze the dynamic stress state around the cavity. The numerical results indicate that the dip angle and axial ratio of the elliptical cavity significantly affected the dynamic stress concentration factor (DSCF), then affected the rock failure. The location of higher DSCF led to a higher possibility of spalling failure. The maximum DSCF remarkably decreased with a decreasing dip angle and increased the axial ratio. In the dynamic loading propagate process, the stress concentration distribution around the cavity formed by a compression stress wave had a certain damaging effect on the destruction of rock around the cavity, and the stress concentration generated by the tensile stress wave was the main factor of the rock fracture, which was most notable in the peak area of the stress concentration.

Excavation-damaged zones (EDZs) induced in underground mining and civil engineering potentially threaten tunnel safety and stability, and increase construction and support costs. In this paper, an investigation of the excavation damaged zone (EDZ) around roadways in Fankou lead-zinc mine in Guangzhou, China is performed by applying a seismic velocity method accompanied by SET-PLT-01 nonmetallic ultrasonic detector. Meanwhile, the in situ stress in the mining area was measured based on the stress relief method with the Swedish high-precision LUT system. The results indicate that the stress field is dominated by the maximum horizontal tectonic stress, and the extents of the EDZ on the roof-floor region are greater than that on the sidewall. In addition, both of the in situ stresses and EDZs show an increasing trend with an increase of depth. Analytical solutions of EDZ around circular openings in the brittle rock mass subjected to non-hydrostatic stress fields are presented in terms of the Mohr−Coulomb and generalized Hoek−Brown criteria, and validated by several cases mentioned above. The extents of EDZ solved by closed-form solutions were found to be in a great agreement with those obtained in the field. Finally, a series of parametric studies are conducted to investigate the effects of cohesion (c), friction angle (φ), geological strength index (GSI), mi, uniaxial compressive strength (σc), and disturbance factor (D) on EDZ. It is shown that the effects of c, φ, GSI, and σc are significant; however, more attention should be paid to consider the dynamic disturbances induced by mechanical drilling, blasting, and seismic waves in tunnel excavations or operations.

The brittle failure of hard rock due to the excavation unloading in deep rock engineering often causes serious problems in mining and tunneling engineering, and the failure process is always affected by groundwater. In order to investigate the effects of stress paths and water conditions on the mechanical properties and failure behavior of rocks, a series of triaxial compression tests were conducted on dry and saturated sandstones under various loading and unloading paths. It was found that when the sandstone rock samples are saturated by water, the cohesion, the internal friction angle and the Young’s modulus will decrease but the Poisson′s ratio will increase. The fracturing characteristics of the sandstone specimens are related to the initial confining pressure, the stress paths and the water conditions from both macroscopic and microscopic viewpoints. The failure of sandstone in unloading test is more severe than that under loading test, particularly for dry sandstone samples. In unloading test, the energy is mainly consumed for the circumferential deformation and converted into kinetic energy for the rock bursts. The sandstone is more prone to produce internal cracks under the effect of water, and the absorbed energy mainly contributes to the damage of rock. It indicates that the possibility of rockburst in saturated rock is lower than the samples in dry condition. It is important to mention that water injection in rock is an effective way to prevent rockburst in deep rock engineering.

Bioactive composites that enable the formation of calcium phosphates have received increased attention over the last decade, in the development of osteoconductive biomaterials for orthopaedic applications. In this work, tripolyphosphate (TPP)-cross-linked chitosan/gelatin composites (TPP-CG) were prepared for the growth of shape- and size-controlled calcium phosphates on/in the composites. The mineralization pattern of the composites, after soaking in the Ca(OH)2 aqueous solution, clearly demonstrated oriented, needle-like nanocrystallites of calcium phosphates in the matrix with especially high Ca/P molar ratio (3.98) as detected by energy dispersive X-ray spectroscopy (EDX) analysis. Subsequent to mineralization in a simulated body fluid (SBF), the mineralized composites showed micro-scaled spherical aggregates deposited on the surface and granule-like nanocrystallites grew in the matrix. The Ca/P molar ratio (1.72) and X-ray diffraction pattern of the nanocrystallites grown in the composites were similar to those of hydroxyapatite (HAp). Osteoblastic differentiation of ROS cells cultured on the mineralized composites allowed an enhanced expression of the chosen osteogenic marker (alkaline phosphatase, ALPase). These results indicated that the composites mineralized with micro- and nano-scaled calcium phosphates with various structural features make them attractive for bone tissue engineering applications.

Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms.

Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder.