Search Results (19,293)

Metal–polymer hybrid joints are gaining importance as they combine high structural rigidity with a low weight. Additive manufacturing processes such as the laser powder bed fusion process (L-PBF) enable the production of complex metallic lattice structures that allow for form-fitting force transmission between the metal and polymer as mechanical interlock elements. In this work, metal–polymer hybrid compounds with additively manufactured transition zones are systematically investigated and mechanically evaluated. Three different lattice geometries (z4A, z8A, z8V) were fabricated from maraging steel (1.2709) using L-PBF and then hybridised with injection moulding using polypropylene (PP C7069-100NA). Mechanical characterisation was performed by tensile tests according to DIN EN ISO 527, in combination with statistical analyses and an analytical serial three-spring model to determine the homogenised elasticity modulus of the transition zone. The results show significant geometry-related differences in tensile strength, maximum force, and effective stiffness. The A-shaped transition zone geometry (z4A) achieves the highest mechanical performance and up to 82% of the tensile strength of the pure polymer, while the V-shaped transition zone geometry (z8V) has significantly lower load-bearing capacities. Variance analysis shows a dominant geometric influence with effect strength of η² ≈ 0.99. The analytically predicted stiffness values match the experimental results within 5–10%. This work demonstrates a reproducible, simulation-sparse approach to the analysis and design of metal–polymer hybrid connections. Full article

The urban ecosystem constitutes a complex adaptive system comprising interdependent subsystems—environment, population, infrastructure, public services, environmental governance, and socio-economic factors. Conventional system dynamics (SD) modeling relies on expert-derived causal assumptions, which have limitations in objectivity, transferability, and adaptability. To solve these, this study develops a data-driven SD modeling framework that infers causal structures from time-series data of 38 sustainability indicators. The framework integrates multiple causal inference techniques to identify causal relationships among variables, then systematically identifies stock variables and constructs an SD simulation model. Applying it to panel data from 41 cities in China’s Yangtze River Delta (2013–2022), the study characterizes the causal network topology, interaction patterns between subsystems, dominant feedback loops, and temporal evolution trajectories of key stock variables. Results show: (1) There is significant cross-city variation in causal network structure due to differences in urban development and institutional configurations; (2) Environmental conditions are the most frequently affected terminal node with an average normalized causal strength of 0.277, higher than other subsystems; (3) Several cross-subsystem positive and negative feedback loops are identified, highlighting potential path dependencies and intervention-sensitive nodes for sustainable urban transitions. This study provides a replicable, comparable, and scalable framework for urban sustainable development analysis, offering data-driven support for smart city management and policy formulation. Full article

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular condition involving the compression of the median nerve at both wrists, leading to pain, neurological symptoms, and loss of function. This paper proposes a robust machine-learning framework for a randomized crossover clinical trial comparing two physiotherapeutic treatment regimens: stretching followed by myofascial mobilization (S/M) and the reverse sequence (M/S). Instead of making inferences about the superiority of one treatment over another, the treatment regimen serves as a structured analytical label for investigating predictive separability, feature representation, and model stability within a controlled experimental setting. The clinical dataset of 73 patients underwent rigorous preprocessing, including strength feature aggregation and principal component analysis (PCA). Various classifiers were evaluated, with CatBoost achieving an ROC-AUC of 0.985 and a test accuracy of 96.5%, while Random Forest demonstrated strong adversarial robustness with an adversarial accuracy of 96.83%. To assess robustness, clinically constrained perturbations were introduced into the PCA feature space, simulating realistic input variability. The findings indicate that ensemble learning algorithms can capture structured patterns in crossover clinical datasets and remain stable under low-magnitude adversarial perturbations. The study underscores the importance of robustness evaluation and interpretability when applying machine learning models to biomedical data, particularly in small and well-structured clinical cohorts. Full article

This study presents the engineering design and system-level modeling of a high-frequency power supply architecture for electrostatic precipitators intended to improve particulate removal efficiency and operational stability. Atmospheric air pollution by fine particulate matter (PM2.5) remains one of the most critical challenges in environmental protection and public health. Although electrostatic precipitators (ESPs) are widely used for industrial gas cleaning, the efficiency and stability of conventional 50 Hz power supplies are limited under conditions of strongly nonlinear corona discharge and high-resistivity dust. This paper presents the development and investigation of an advanced high-frequency power supply system for electrostatic precipitators based on a coupled electrical–electrophysical mathematical model. The work follows an engineering design methodology that integrates converter topology selection, electrophysical modeling of corona discharge, and control-oriented system optimization. The proposed model provides a unified description of electric field formation, space charge accumulation, ion transport, and particle motion in the corona discharge region. The simulation results show that in the operating voltage range of 10–100 kV, the electric field strength reaches (2–5)·10⁶ V/m, the ion concentration stabilizes in the range of 10¹³–10¹⁵ m⁻³, and the particle drift velocity increases from approximately 0.05 to 0.3 m/s, leading to an increase in collection efficiency from about 55% to 93%. It is demonstrated that the proposed system ensures stable output voltage regulation within ±2.5–5% even under strongly nonlinear load conditions. The use of an LC output filter (C = 1–10 nF, L = 10–100 mH) reduces the voltage ripple from about 14% to 1.4–4.8% and significantly improves the transient response. In addition, adaptive adjustment of the pulse repetition frequency in the range of 10–200 kHz makes it possible to reduce energy consumption by 12–18% while simultaneously increasing the collection efficiency by 8–15%. The obtained results confirm that the proposed high-frequency power supply architecture provides a physically well-founded and energy-efficient solution for improving the environmental performance and operational stability of electrostatic precipitators. Full article

Carbonate reservoirs in the Shunbei area develop pronounced fracture networks after acidized hydraulic fracturing and thus have the potential to be repurposed as underground gas storage (UGS) after hydrocarbon depletion. Characterizing their mechanical behavior is essential for safe UGS operation; however, deep to ultra-deep natural cores are difficult to obtain, and conventional macroscopic tests often cannot provide parameters that meet engineering requirements. To address this issue, nanoindentation combined with QEMSCAN (Quantitative Evaluation of Minerals by Scanning Electron Microscopy) was employed to quantify microscale mineral distributions and the mechanical properties of the major constituents. The investigated rock is calcite-dominated (89.62%), with minor quartz (9.89%) and trace feldspar-group minerals (1.89%). Minerals are randomly embedded, and soft–hard phase boundaries are widely distributed. A finite–discrete element method (FDEM) model was then constructed and calibrated in ABAQUS. The discrepancies in uniaxial compressive strength and elastic modulus relative to laboratory results were 6.51% and 9.91%, respectively, indicating good agreement in both mechanical response and failure mode. Parametric analyses using three additional models with different mineral proportions show that damage preferentially initiates at mineral phase boundaries and stress concentration zones induced by end constraints. Microcracks then propagate and coalesce into a dominant compressive–shear band, and final failure is mainly governed by slip along the shear band with localized tensile cracking. With increasing quartz and feldspar contents, enhanced heterogeneity and a higher density of phase boundaries lead to a higher density of crack nucleation sites and increased crack branching, and the failure pattern transitions from a single shear-band–controlled mode to a more network-like fracture system. Moreover, macroscopic strength is not determined solely by the intrinsic strength of individual minerals; heterogeneity and phase-boundary characteristics strongly govern microcrack behavior, such that higher hard-phase contents may result in a lower peak strength. Full article

Hair loss affects over half of adults by age 70 and represents a major determinant of overall hair health, imposing significant psychosocial burden across genders. Nutritional factors play a critical role in follicle biology, yet targeted strategies remain underexplored. This comprehensive review examines five key hair-constituent macromolecules—type I collagen, elastin, keratin, ceramides, and melanin—and their physiological and clinical impacts on hair structure, density, shining, and growth. We conducted a structured literature search in PubMed and Google Scholar through January 2025, selecting in vitro studies, animal experiments, and human clinical trials that evaluated each macromolecule’s effects on follicular function and hair fiber integrity. Type I collagen enhances dermal papilla cell proliferation, upregulates Wnt/β-catenin and growth factors, and improves hair thickness and breakage resistance in randomized controlled trials. Keratin hydrolysates replenish cortical protein, reinforce disulfide cross-links, and reduce telogen shedding, with clinical studies demonstrating 30–50% decreases in hair loss and gains in tensile strength. Oral ceramide formulations restore the cuticular lipid barrier, shift follicles toward anagen, and increase hair density in double-blind trials. Although direct clinical data on melanin supplementation are lacking, ex vivo and animal models confirm its role as a UV-protective pigment, preserving keratin integrity and color fastness. Together, these macromolecules constitute a coherent framework for hair health, and clinical studies increasingly provide evidence that their combined or parallel application can meaningfully enhance hair density, strength, shine, and resilience. Full article

The aim of this work was to develop a model using Artificial Neural Networks (ANN) to predict stem cutting parameters for giant miscanthus. Experimental studies were conducted to determine biometric traits: maximum stem diameter (Dmax), minimum stem diameter (Dmin), stem wall thickness (THwall), and strength parameters (cutting force, cutting work) for two giant miscanthus genotypes, depending on the internode number (NrNod) and water content (MC). A total of 600 measurement results were obtained, which were randomly divided into training (60%), test (20%), and validation (20%) subsets. Two semantic models were adopted: one for predicting stem cutting force (ann1) and one for predicting cutting work (ann2). The independent variables (ANN inputs) were: Gen, MC, NrNod, Dmax, Dmin, and THwall. The ANN creation process was performed using Statistica Neural Networks. For each of the two semantic models (ANN1 and ANN2), 100 neural networks were developed, with the top 10 ANNs retained for further analysis. The criterion for selecting the best neural network was the root mean square error (RMSE) for the test subset. For ANN1, the RMSE values varied from 6.89 N to 8.70 N. For ANN2, the RMSE values varied from 0.086 J to 0.102 J. For the most accurate ANN1-03 (MLP 7-10-1), used to predict grass cutting force, the RMSE values were 6.46 N–6.89 N–4.70 N for the training, test, and validation subsets. For the most accurate ANN2-02 (MLP 7-10-1), used to predict grass cutting work, the RMSE values were 0.0646 J–0.0857 J–0.0596 J for the training, test, and validation subsets. Full article

Gout is the most common form of inflammatory arthritis in men, driven by hyperuricemia and the deposition of monosodium urate (MSU) crystals. The innate immune response to these crystals leads to acute inflammatory episodes, called flares, characterized by intense joint pain, swelling, and temporary disability. Although gout flares are self-limiting, they impose a considerable burden on patients’ quality of life and contribute to increased healthcare utilization. A detailed understanding of the inflammatory processes triggered by MSU crystals is critical for developing targeted therapies to prevent and manage flares effectively. This review provides an overview of experimental models used to study the inflammatory phase of gout, with a focus on both in vivo and in vitro models of MSU crystal-induced inflammation. We concentrate on models that reproduce the acute inflammatory response following MSU crystal deposition, including the air pouch, intraarticular injection, and peritonitis rodent models, alongside the larval zebrafish model. In addition, we discuss in vitro approaches using primary immune cells and cell lines. We discuss the strengths, limitations, and translational relevance of these models and highlight some examples of how they have contributed to our understanding of the etiology of gout. Of note, models of hyperuricemia are not included here as these have been extensively reviewed elsewhere. Full article

In this study, we explore the stability of shield tunnel faces excavated entirely within confined aquifers through a combined physical investigation. A series of orthogonally designed model tests were performed to examine how the hydraulic head difference (Δh) and aquitard thickness (M) jointly influence face stability and seepage behavior. Our results reveal a distinct concave-downward pore-pressure profile and a steep hydraulic gradient immediately ahead of the excavation face. Excavation-induced stress redistribution was largely restricted to the aquifer, whereas the overlying aquitard exhibited negligible disturbance due to its low permeability and higher strength. The evolution of stress disturbance followed a three-stage process encompassing initial disturbance, progressive development, and large-scale destabilization. Deformation contours exhibited a conical failure zone with normalized width and height ranging from 0.7D to 1.0D and 1.7D to 1.86D. Surface settlements remained within ±1 mm, confirming that deformation was effectively confined below the aquitard. Numerical simulations reproduced the overall hydro-mechanical response, validating the experimental observations but slightly overpredicting support pressures due to the absence of arching effects. The findings highlight Δh/M as the dominant control parameter, with aquitard thickness exerting a moderating influence. Full article

This paper presents a theoretical study on the seismic behavior and working mechanisms of eccentrically braced composite frames with vertical shear links. A theoretical model is established based on structural mechanics principles to analyze the internal force distribution and deformation patterns under lateral loading. Formulas for the lateral stiffness, bending moments in beams and columns, and joint rotations are derived. A multi-stage theoretical skeleton curve model is proposed, identifying key points such as cracking, yielding, peak strength, and failure, along with corresponding methods for calculating load and displacement values. The theoretical results show good agreement with experimental data, effectively predicting the structural stiffness, load-bearing capacity, and deformation behavior. Key design parameters affecting structural performance are identified, including the beam–column linear stiffness ratio, geometric properties of the shear link, and brace stiffness. The study provides a theoretical basis and practical methodology for the seismic design of such structures. Full article

The sustainable management of balneological resources is vital for the development of eco-friendly health tourism and regional economic stability. This study presents a comprehensive physicochemical and eco-toxicological characterization of the therapeutic peloids (mud) from Lake Maly Akkol, which is located in the Zhambyl region of Kazakhstan. Utilizing an integrated approach of laboratory analysis and Python-based statistical modeling, we evaluated the resource’s clinical potential and environmental safety. The results identify the deposit as a high-quality sulfide–silt peloid with a mean humidity of 66.91% (95% CI: [65.21, 68.60]) and a mineralization level of 11.21 g/dm³ (95% CI: [10.84, 11.57]). Statistical validation using one-sample t-tests confirmed that critical therapeutic indicators, including shear strength ($\mu$ = 2593.72 dyne/cm²) and total sulfide content ($\mu$ = 0.079%), are significantly aligned with international balneological standards (p < 0.05). Eco-toxicological screening for heavy metals revealed that Lead (37.03 mg/kg) and Cadmium (0.06 mg/kg) remain well below safety thresholds, ensuring the resource’s “clean” environmental profile. These findings establish a statistically robust “Digital Quality Passport” for the Lake Maly Akkol deposit, providing the scientific foundation necessary for its sustainable industrial utilization and long-term ecological preservation. Full article

Additive manufacturing, particularly 3D printing, is increasingly shaping the production of polymer-based components, enabling complex geometries and tailored functional performance. Yet, predicting their mechanical behavior remains challenging due to material anisotropy and sensitivity to processing conditions. This work presents an exploratory study designed to provide the experimental basis for the development and calibration of predictive models of mechanical properties in 3D-printed components. Standard ISO 527-2 Type 1A specimens were fabricated using thermoplastic PLA (polylactic acid) with systematic variations in layer orientation, infill overlap, and printing velocity. Mechanical characterization was carried out through uniaxial tensile testing to determine tensile strength and stiffness of the material specimens, while scanning electron microscopy (SEM) provided complementary insights into interlayer bonding, filament alignment, porosity, and fracture morphology. Results showed that material type and processing strategies strongly influenced mechanical response, with SEM highlighting microstructural features that govern interlayer adhesion and failure mechanisms. These findings contribute to a deeper understanding of process–structure–property relationships in additive manufacturing and establish the groundwork for predictive model development. Ongoing efforts will integrate these experimental insights into numerical simulations employing homogenized material models, thereby enhancing design optimization and reliability of 3D-printed structural components. Full article

Lyapunov-exponent-based diagnostics are widely used to quantify deterministic chaos in optically injected semiconductor lasers (OISLs). In most numerical implementations, the optical field is represented either in phasor coordinates $(A,\psi ,N)$ or in Cartesian quadrature coordinates $(X,Y,N)$. Although these representations are mathematically related through a smooth coordinate transformation away from vanishing field amplitude, their numerical realizations can exhibit markedly different robustness in variational calculations, directly impacting the reliability of Lyapunov exponent estimation and chaoticity maps. In this work, we present a systematic assessment of the numerical reliability of Lyapunov-based chaos analysis in master-slave optically injected semiconductor lasers using both phasor and quadrature formulations. The full Lyapunov spectrum was computed via a noise-free variational method that integrates the nonlinear dynamics together with the corresponding Jacobian equations using a fourth-order Runge-Kutta scheme combined with periodic QR orthonormalization. High-resolution Lyapunov maps were constructed in the injection strength-frequency detuning parameter space, and the consistency between both formulations was quantitatively evaluated. While both approaches reproduce the overall structure of chaotic and non-chaotic regions, the phasor formulation may generate spurious positive Lyapunov exponents in regimes where the optical field amplitude approaches low values. These discrepancies originate from singular terms proportional to $1/A$ and $1/A^2$ in the variational Jacobian of the phasor model, which can lead to numerical amplification and artificial chaotic signatures. The quadrature formulation avoids these singularities and provides numerically stable and physically consistent Lyapunov spectra across the explored parameter space. The results establish practical guidelines for robust chaos quantification in optically injected semiconductor lasers and highlight the importance of representation choice in variational Lyapunov analysis of nonlinear photonic systems. Full article

It is necessary to understand the complicated interplay between diseases and medicinal plants to find new curing agents that may be used in natural sources. Nevertheless, the state of interaction between diseases and plants today is not fully developed yet, and the potentially productive plant-based treatment can hardly be identified rationally. In order to elaborate on this challenge, we will offer a heterogeneous network approach to the prediction of novel disease–plant associations by using the Random Walk with Restart (RWR) algorithm. The framework combines three significant relational networks, including (i) a disease–plant association network, which has been built using curated literature and biological databases, (ii) a disease–disease similarity net, which is constructed using shared symptoms and therapeutic profiles, and (iii) a plant–plant similarity net using phytochemical and functional similarities. These elements are integrated into a homogeneous graph that is heterogeneous in nature, and thus, information flows through related nodes. The model begins by finding RWR between known disease or plant nodes and develops the network by exploring the graph further to make estimates of the probability of association between disease and plant networks that were not previously connected. Experimental tests show that the proposed model has an excellent predictive ability, ROC-AUC of 0.9987, PR-AUC equal to 0.915, and Precision = 10 of 1.0, significantly better than the results of the base models, including Random- and Degree-based models. The bootstrap analysis supported the strength of the model as the mean ROC-AUC was 0.9987 with a standard deviation of 0.00051. The suggested structure offers an effective computational methodology to systematically explore disease–plant interactions to aid in finding novel herbal drugs to treat diseases and speed up the drug discovery process by means of inference based on networks. Full article

For a deep-buried complex cavern with complex geological conditions, it is difficult to determine the critical warning deformation of surrounding rock. A determination method for warning deformations based on rock strength is proposed to study the warning status of the surrounding rock of the Baihetan left-bank underground powerhouse. Three warning levels—blue, yellow, and red—are numerically established based on crack initiation stress, dilatancy stress, and uniaxial compressive strength of the rock mass. These warning deformations are influenced not only by the critical stresses but also by the cavern shape, rock position, deformation properties and in situ stresses. The in situ stresses were inversely analyzed by a three-dimensional geological model orthogonal to the principal stresses and present a high determination coefficient of 0.834 with the measured results. However, the complex geological conditions could bring great uncertainties to the simulation results and significantly reduce the warning deformations. Thus, the monitored deformations that reflect these uncertainties, instead of the simulated stresses or deformations, were used to predict the warning state of the surrounding rock, which was analyzed by comparing the on-site monitored deformations with the critical warning deformations. The warning results demonstrated that the proposed methodology enables prediction of warning location, timing and grades, and 85.9% of monitoring points obtained correct warning signals. Full article