Search Results (242)

The layered composite roof of a coal mine roadway exhibits heterogeneity, with pronounced variations in layer thickness and strength. Fully grouted rock bolts installed in such layered roofs usually penetrate two or more strata and bond with them to form an integrated anchorage system. Roof failure typically initiates in the shallow strata and progressively propagates to deeper layers; thus, the mechanical properties of the rock at the free surface critically influence the overall stability of the layered roof and the load-transfer behavior of the bolts. In this study, a layered rock mass model was developed using three-dimensional particle flow code (PFC3D), and a triaxial loading scheme with a single free surface was applied to investigate the effects of free-surface rock properties, support parameters, and confining pressure on the load-bearing performance of the layered rock mass. The main findings are as follows: (1) Without support, the ultimate bearing capacity of a hard-rock-free-surface specimen is about 1.2 times that of a soft-rock-free-surface specimen. Applying support strengths of 0.2 MPa and 0.4 MPa enhanced the bearing capacity by 29–38% and 46–75%, respectively. (2) The evolution of axial stress in the bolts reflects the migration of the load-bearing core of the anchored body. Enhancing support strength improves the stress state of bolts and effectively mitigates the effects of high-stress conditions. (3) Under loading, soft rock layers exhibit greater deformation than hard layers. A hard-rock free surface effectively resists extrusion deformation from deeper soft rocks and provides higher bearing capacity. Shallow free-surface failure is significantly suppressed in anchored bodies, and “compression arch” zones are formed within multiple layers due to bolt support. Full article

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems. Full article

Three-dimensional concrete printing (3DCP) is an emerging technology that improves design flexibility and material efficiency in construction. However, widespread adoption of 3DCP requires overcoming key material challenges. These include controlling rheology for pumpability and buildability and achieving sufficient mechanical strength. This paper provides a comprehensive review of the application of nanoclays (NCs) as a key admixture to address these challenges. The effects of three primary NCs (attapulgite (ATT), bentonite (BEN), and sepiolite (SEP)) on the fresh- and hardened-state properties of printable mortars are systematically analyzed. This review summarize findings on how NCs enhanced thixotropy, yield stress, and cohesion, which are critical for shape retention and the successful printing of multilayered structures. Quantitative analysis reveals that optimized dosages of NCs can increase compressive strength by up to 34% and flexural strength by up to 20%. For enhancing rheology and printability, a dosage of approximately 0.5% by binder weight is often suggested for ATT and SEP. In contrast, BEN can be used at higher replacement levels (up to 20%) to also function as a supplementary cementitious material (SCM), though this significantly impacts workability. This review consolidates the current knowledge to provide a clear framework for selecting appropriate NCs and dosages to develop high-performance, reliable, and sustainable materials for 3DCP applications. Full article

This study presents a comparative analysis of three metal additive manufacturing processes: selective laser melting (SLM), also known as powder bed fusion (PBF); binder jetting (BJ); and atomic diffusion additive manufacturing (ADAM), a form of Material Extrusion (MEX). It focuses on the geometric and dimensional accuracy of ADAM-fabricated 17-4 PH stainless steel components, while AISI 316L stainless steel is the benchmark material for BJ and SLM technologies. In addition to dimension and geometry inspections, this study also measures the distribution of residual stresses and microstructural features of the printed components. Residual stresses were determined quantitatively to identify the internal state of stress developed because of each processing technology. The results reveal significant differences in dimensional accuracy, residual stress profiles, surface roughness, and microstructural characteristics among the three additive manufacturing technologies. The observed trends and correlations provide valuable guidance for selecting the most appropriate additive manufacturing technique based on required accuracy, mechanical properties, and product complexity. Full article

This study presents a numerical investigation into the heart valve through a fluid–structure interaction (FSI) framework using a two-dimensional, steady-state, Newtonian flow assumption. While simplified, this approach captures core biomechanical effects and provides a baseline for future extension toward non-Newtonian, pulsatile, and three-dimensional models. The analysis focuses on the influence of magnetic field intensity characterized by the Hartmann number (Ha) and flow regime defined by the Reynolds number (Re) on critical hemodynamic parameters, including wall shear stress (WSS), velocity profiles, and pressure gradients in the valve region. The results demonstrate that stronger magnetic fields significantly stabilize intravalvular flow by suppressing recirculation zones and reducing flow separation distal to valve constrictions, offering protective hemodynamic benefits and serving as a non-invasive method to modulate vascular behavior and reduce the risk of cardiovascular pathologies such as atherosclerosis and hypertension. Full article

Aiming at the lightweight design of a bridge-shed integration structure, this paper presents a three-layered absorbing system in which a part of the sand cushion is replaced by expanded polystyrene (EPS) geofoam and the reinforced concrete (RC) protective slab is arranged above the sand cushion to enhance the composite system’s safety. A three-dimensional Smoothed Particle Hydrodynamics–Finite Element Method (SPH-FEM) coupled numerical model is developed in LS-DYNA (Livermore Software Technology Corporation, Livermore, CA, USA, version R13.1.1), with its validity rigorously verified. The dynamic response of rockfall impacts on the shed slab with composite cushions of various thicknesses is analyzed by varying the thickness of sand and EPS materials. To optimize the cushion design, a specific energy dissipation ratio (SEDR), defined as the energy dissipation rate per unit mass (η/M), is introduced as a key performance metric. Furthermore, the complicated interactional mechanism between the rockfall and the optimum-thickness composite system is rationally interpreted, and the energy dissipation mechanism of the composite cushion is revealed. Using logistic regression, the ultimate stress state of the reactive powder concrete (RPC) slab is methodically analyzed, accounting for the speed and mass of the rockfall. The results are indicative of the fact that the composite cushion not only has less dead weight but also exhibits superior impact resistance compared to the 90 cm sand cushions; the impact resistance performance index SEDR of the three-layered absorbing system reaches 2.5, showing a remarkable 55% enhancement compared to the sand cushion (SEDR = 1.61). Additionally, both the sand cushion and the RC protective slab effectively dissipate most of the impact energy, while the EPS material experiences relatively little internal energy build-up in comparison. This feature overcomes the traditional vulnerability of EPS subjected to impact loads. One of the highlights of the present investigation is the development of an identification model specifically designed to accurately assess the stress state of RPC slabs under various rockfall impact conditions. Full article

Static analysis is conducted for functionally graded material (FGM) spherical shells under thermo-mechanical loads, based on the three-dimensional thermo-elasticity theory. The material properties, which vary with both the radial coordinate and temperature, introduce nonlinearity to the problem. To address this, a layer model is proposed, wherein the shell is discretized into numerous concentric spherical layers, each possessing uniform material properties. Within this framework, the nonlinear heat conduction equations are first solved iteratively. The resulting temperature field is then applied to the thermo-elastic equations, which are subsequently solved using a combined state space and transfer matrix method to obtain displacement and stress solutions. Comparison with existing literature results demonstrates good agreement. Finally, a parametric study is presented to investigate the effects of material temperature dependence and gradient index on the thermo-mechanical behaviors of the FGM spherical shells. Full article

The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables. Full article

Three-dimensional (3D) concrete printing (3DCP) has gained significant attention over the last decade due to its many claimed benefits. The absence of effective real-time quality control mechanisms, however, can lead to inconsistencies in extrusion, compromising the integrity of 3D-printed structures. Although the importance of quality control in 3DCP is broadly acknowledged, research lacks systematic methods. This research investigates the feasibility of using ultrasonic pulse velocity (UPV) as a practical, in situ, real-time monitoring tool for 3DCP. Two different groups of binders were investigated: limestone calcined clay (LC³) and zeolite-based mixes in binary and ternary blends. Filaments of 200 mm were extruded every 5 min, and UPV, pocket hand vane, flow table, and viscometer tests were performed to measure pulse velocity, shear strength, relative deformation, yield stress, and plastic viscosity, respectively, in the fresh state. Once the filaments presented printing defects (e.g., filament tearing, filament width reduction), the tests were concluded, and the open time was recorded. Isothermal calorimetry tests were conducted to obtain the initial heat release and reactivity of the supplementary cementitious materials (SCMs). Results showed a strong correlation (R² = 0.93) between UPV and initial heat release, indicating that early hydration (ettringite formation) influenced UPV and determined printability across different mixes. No correlation was observed between the other tests and hydration kinetics. UPV demonstrated potential as a real-time monitoring tool, provided the mix-specific pulse velocity is established beforehand. Further research is needed to evaluate UPV performance during active printing when there is an active flow through the printer. Full article

In response to the high-performance requirements of concrete materials under complex triaxial stress states and water-containing environments in marine engineering, this study focuses on water-containing basalt fiber-reinforced concrete (BFRC). Uniaxial compression and splitting tensile tests were conducted on specimens with different fiber contents (0.0%, 0.05%, 0.10%, 0.15%, and 0.20%) to determine the optimal fiber content of 0.1%. The compressive strength of the concrete with this fiber content increased by 13.5% compared to the control group without fiber, reaching 36.90 MPa, while the tensile strength increased by 15.9%, reaching 2.33 MPa. Subsequently, NMR and SEM techniques were employed to analyze the internal pore structure and micro-morphology of BFRC. It was found that an appropriate amount of basalt fiber (content of 0.1%) can optimize the pore structure and form a reticular three-dimensional structure. The pore grading was also improved, with the total porosity decreasing from 7.48% to 7.43%, the proportion of harmless pores increasing from 4.03% to 4.87%, and the proportion of harmful pores decreasing from 1.67% to 1.42%, thereby significantly enhancing the strength of the concrete. Further triaxial compression tests were conducted to investigate the mechanical properties of BFRC under different confining pressures (0, 3, and 6 MPa) and water contents (0%, 1%, 2%, and 4.16%). The results showed that the stress–strain curves primarily underwent four stages: initial crack compaction, elastic deformation, yielding, and failure. In terms of mechanical properties, when the confining pressure increased from 0 MPa to 6 MPa, taking dry sandstone as an example, the peak stress increased by 54.0%, the elastic modulus increased by 15.7%, the peak strain increased by 37.0%, and the peak volumetric strain increased by 80.0%. In contrast, when the water content increased from 0% to 4.16%, taking a confining pressure of 0 MPa as an example, the peak stress decreased by 27.4%, the elastic modulus decreased by 43.2%, the peak strain decreased by 59.3%, and the peak volumetric strain decreased by 106.7%. Regarding failure characteristics, the failure mode shifted from longitudinal splitting under no confining pressure to diagonal shear under confining pressure. Moreover, as the confining pressure increased, the degree of failure became more severe, with more extensive cracks. However, when the water content increased, the failure degree was relatively mild, but it gradually worsened with further increases in water content. Based on the CDP model, a numerical model for simulating the triaxial compression behavior of BFRC was developed. The simulation results exhibited strong consistency with the experimental data, thereby validating the accuracy and applicability of the model. Full article

A meshless, quasi-convex reproducing kernel particle framework for three-dimensional steady-state thermomechanical coupling problems is presented in this paper. A meshfree, second-order, quasi-convex reproducing kernel scheme is employed to approximate field variables for solving the linear Poisson equation and the elastic thermal stress equation in sequence. The quasi-convex reproducing kernel approximation proposed by Wang et al. to construct almost positive reproducing kernel shape functions with relaxed monomial reproducing conditions is applied to improve the positivity of the thermal matrixes in the final discreated equations. Two numerical examples are given to verify the effectiveness of the developed method. The numerical results show that the solutions obtained by the quasi-convex reproducing kernel particle method agree well with the analytical ones, with a slightly better-improved numerical accuracy than the element-free Galerkin method and the reproducing kernel particle method. The effects of different parameters, i.e., the scaling parameter, the penalty factor, and node distribution on computational accuracy and efficiency, are also investigated. Full article

It is projected that the world population will quadruple over the next century, and to meet future food demands, agricultural production will need to increase by 70%. Therefore, there has been a transition from traditional farming methods to autonomous modern agriculture. One such modern technique is aeroponic farming, in which plants are grown without soil under controlled and hygienic conditions. In aeroponic farming, plants are significantly less affected by climatic conditions, infectious diseases, and biotic and abiotic stresses, such as pest infestations. Additionally, this method can reduce water, nutrient, and pesticide usage by 98%, 60%, and 100%, respectively, while increasing the yield by 45–75% compared to traditional farming. In this study, a three-dimensional industrial design of an innovative aeroponic plant growth chamber was presented for use by individuals, researchers, and professional growers. The proposed chamber design is modular and open to further innovation. Unlike existing chambers, it includes load cells that enable real-time monitoring of the fresh weight of the plant. Furthermore, cameras were integrated into the chamber to track plant growth and changes over time and weight. Additionally, RGB power LEDs were placed on the inner ceiling of the chamber to provide an optimal lighting intensity and spectrum based on the cultivated plant species. A customizable chamber design was introduced, allowing users to determine the growing tray and nutrient nozzles according to the type and quantity of plants. Finally, system models were developed for temperature control of the chamber. Temperature control was implemented using a proportional-integral-derivative controller optimized with particle swarm optimization, radial movement optimization, differential evolution, and mayfly optimization algorithms for the gain parameters. The simulation results indicate that the temperatures of the growing and feeding chambers in the cabinet reached a steady state within 260 s, with an offset error of no more than 0.5 °C. This result demonstrates the accuracy of the derived model and the effectiveness of the optimized controllers. Full article

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices. Full article

The increasing prevalence of stress-related disorders necessitates accurate and efficient detection methods for timely intervention. This study explored the potential of heart rate variability as a biomarker for detecting mental stress using a publicly available dataset. A total of 93 heart rate variability features extracted from electrocardiogram signals were analyzed to differentiate stress from non-stress conditions. Our methodology involved data preprocessing, feature computation, and three feature selection strategies—filter-based, wrapper, and embedded—to identify the most relevant heart rate variability features. By leveraging Recursive Feature Elimination combined with Nested Leave-One-Subject-Out Cross-Validation, we achieved a peak F1 score of 0.76. The results demonstrate that two heart rate variability features—the median absolute deviation of the RR intervals (the time elapsed between consecutive R-waves on an electrocardiogram), which is normalized by the median, and the normalized low frequency power—consistently distinguished the stress states across multiple classifiers. To assess the robustness and generalizability of our best-performing model, we evaluated it on a completely unseen dataset, which resulted in an average F1 score of 0.63. These findings emphasize the value of targeted feature selection in optimizing stress detection models, particularly when handling high-dimensional datasets with potentially redundant features. This study contributes to the development of efficient stress monitoring systems, paving the way for improved mental health assessment and intervention. Full article

We have previously shown that the mitochondrial respiratory chain (RC) can switch between the following two states: (i) an “ATP-producing” state characterized by the low production of reactive oxygen species (ROS), the vigorous translocation of hydrogen ions (H⁺), and the storage of energy from the H⁺ gradient in the form of ATP, and (ii) an “ROS-producing” state, where the translocation of H⁺ is slow but the production of ROS is high. Here, we suggest that the RC transition from an ATP-producing to an ROS-producing state initiates a mitochondrial permeability transition (MPT) by generating a burst of ROS. Numerous MPT activators induce the transition of the RC to an ROS-producing state, and the ROS generated in this state activate the MPT. The MPT, in turn, induces changes in conditions that are necessary for the RC to return to an ATP-producing state, decreasing the ROS production rate and restoring the normal permeability of the inner membrane. In this way, the transient MPT prevents cell damage from oxidative stress that would occur if the RC remained in an ROS-producing state. It is shown that an overload of glutamate, which enters through excitatory amino acid transporters (EAATs), induces the RC to switch to an ROS-producing state. Subsequent MPT activation causes a transition back to an ATP-producing state. The model was used to predict the spatial–temporal dynamics of glutamate concentrations and H₂O₂ production rates in a three-dimensional digital phantom of nervous tissue. Full article