Search Results (92)

Background/Objectives: Standard mechanical compression applied during screening mammography is a primary barrier that reduces patient compliance. Current guidelines attempt to standardize compression based solely on the one-dimensional “breast thickness” measured by the device. This study aimed to investigate the effects of three-axis anatomical breast dimensions, applied compression force, menstrual cycle phases, and BI-RADS breast density patterns on pain scores during mammography within a comprehensive biomechanical model. Methods: This retrospective cohort study included 443 female patients who underwent routine screening or diagnostic mammography. Patients with a history of breast implants, lactation, or prior breast surgery that could alter tissue biomechanics were excluded. Maximum pain scores (1–10 on a Visual Analog Scale [VAS]) were recorded. Transverse, anteroposterior, and superoinferior breast biometric measurements for each patient were calculated using advanced radiological workstations. Data were analyzed using One-Way ANOVA and Multiple Linear Regression (OLS) models. Results: The mean age of the participants was 49.7 ± 9.4 years, the mean applied compression force was 62.4 ± 10.3 N, and the mean pain score was 2.03 ± 2.12 (range: 1–10). The multiple linear regression analysis was statistically significant overall (F = 2.516, p = 0.015). Having a BI-RADS Type D (extremely dense) breast pattern was identified as the strongest independent factor associated with an increased pain score (p = 0.082, coefficient = 1.219). Age showed a trend toward a negative effect on pain (p = 0.072), while compression force showed a trend toward a positive effect (p = 0.067). Conversely, breast thickness (p = 0.231) and the three-dimensional mean breast size index (p = 0.568) demonstrated no independent predictive power. The menstrual cycle phase did not reach independent significance in the multivariate regression model (p = 0.117); however, non-parametric univariate analysis revealed a significant difference in pain across hormonal groups (Kruskal–Wallis H = 10.04, p = 0.039), with actively menstruating and luteal-phase women reporting higher pain than menopausal women. Conclusions: The pain experienced during mammography depends on the internal fibroglandular architecture (elasticity and stiffness) of the tissue rather than its external volumetric dimensions. Notably, neither device-measured breast thickness nor manually calculated three-dimensional breast dimensions independently predicted pain, challenging the widespread assumption that breast size drives mammographic discomfort. “One-size-fits-all” or thickness-based compression strategies should be abandoned in routine practice. Instead, “personalized compression” protocols that prioritize patient comfort without compromising image quality should be developed, particularly for younger patients and those with BI-RADS Type D, and to a lesser extent Type C, density patterns. Full article

Background: Out-of-hospital cardiac arrest (OHCA) remains a major public health problem. Many patients contact primary health care (PHC) services shortly before cardiac arrest, yet data on PHC staff preparedness to provide guideline-concordant basic life support (BLS) remain limited. This study assessed BLS knowledge and chest compression quality among medical and non-medical PHC staff. Methods: This multicentre cross-sectional simulation-based study was conducted in Poznań and Poznań County, Poland. PHC staff with direct patient contact were included (n = 162). Assessment comprised an author-developed 15-item knowledge test based on European Resuscitation Council guidelines and a two-minute continuous chest compression trial on a Resusci Anne QCPR manikin. Correlations were analysed using Spearman’s rank correlation coefficient, group differences using the Kruskal–Wallis test with Dunn–Bonferroni post hoc comparisons, and predictors using multivariable linear regression. Results: The median BLS knowledge score was 9/15 points (mean 8.74). Mean chest compression depth was 41.3 mm, below the recommended range, with only 23.5% of compressions meeting depth criteria. Correct compression rate was maintained in 30.2% of compressions, and full chest recoil was observed in 55.0% of attempts. Age was negatively correlated with compression rate. In participant-level regression, higher BLS knowledge was associated with better QCPR performance; however, this association was attenuated and no longer statistically significant in mixed-effects models accounting for clustering by practice. Conclusions: PHC staff demonstrated gaps in BLS knowledge and inadequate simulated chest compression performance, particularly regarding compression depth and rate. These findings support recurrent, simulation-based BLS training for all PHC personnel. Full article

To address the damage and failure of electromechanical structures such as Printed Circuit Board (PCB) modules and battery assemblies under multiple impacts, this study combined experimental and modeling approaches to quantitatively investigate the influence of target material mechanical properties on impact acceleration characteristics. Quasi-static tensile/compression tests, split-Hopkinson pressure bar dynamic compression tests, and sequential multiple-target impact experiments were conducted on nine metallic materials, providing constitutive parameters and impact response data. Variance analysis revealed that material type significantly affected acceleration characteristics (p ≤ 1.62 × 10⁻⁵), whereas the target position in the impact sequence was statistically insignificant (p ≥ 0.89). Quantitative prediction models were established for different acceleration characteristics: Ridge regression (α = 0.1) was employed for Peak 1–Peak 3, Duration 1, and Duration 3, while linear regression was used for Duration 2. The results quantitatively demonstrated that the elastic modulus was positively associated with both peak acceleration and duration, while dynamic compressive yield strength exhibited a significant negative influence. This work establishes a preliminary quantitative predictive framework that provides guidance for target material selection in sequential multiple-target impact experiments and offers an experimental approach for generating tunable overload responses in high-intensity impact testing of electromechanical components. Full article

Coal masses with well-developed cleats exhibit pronounced heterogeneity and anisotropy, and obtaining intact cores for mechanical testing remains a persistent challenge in engineering practice. Conventional assessments using the Hoek-Brown (HB) criterion rely heavily on empirical geological indices and cannot establish a quantitative correlation between cleat characteristics and rock mass parameters, thereby leading to low accuracy and efficiency in strength evaluation. In this study, numerical coal models are established using the discrete element method (DEM) combined with laboratory mechanical tests, and a series of uniaxial and triaxial compression simulations are conducted. Results reveal that cleat intensity is negatively correlated with uniaxial compressive strength and peak strain, while matrix stiffness and intermediate principal stress positively affect the elastic modulus and strength of coal; the intrinsic mechanical parameters of cleats exert a limited influence on the macroscopic mechanical behavior. A linear correlation between 2D cleat areal density P21 and 3D intensity P32 is verified, and a prediction model for HB parameters m and s based on cleat features is developed. The proposed method only requires profile cleat statistics and a limited number of uniaxial tests to achieve efficient and reliable strength evaluation. It possesses considerable theoretical innovation and practical engineering value. Full article

Accurate prediction of Rate of Penetration (ROP) in carbonate formations remains constrained by the arbitrary selection of geomechanical input parameters in empirical drilling models. This study presents the first systematic field-based evaluation of sixteen geomechanical properties—grouped into three categories: strength parameters (uniaxial compressive strength (UCS), confined compressive strength (CCS), shear strength, thick-walled cylinder strength (TWC), friction angle, and cohesion), elastic moduli (Young’s modulus, shear modulus, bulk modulus, bulk compressibility, dynamic combined modulus (DCM), Poisson’s ratio, brittleness index), and in situ stress parameters (overburden pressure, minimum, and maximum horizontal stresses)—to identify optimal predictors for ROP modeling across PDC bit sizes of 12.25″ and 8.5″. Continuous wireline log data from two vertical carbonate wells in the Middle East (Well A: 1000–3370 m; Well B: 1945 to 3128 m; total intervals of 2370 m and 1183 m, respectively) penetrating formations comprising limestone, dolomite, sandstone, shale, anhydrite, and marly limestone were used. All sixteen geomechanical properties were computed using Interactive Petrophysics (IP) software with lithology-specific empirical correlations and validated against laboratory core measurements (R² = 0.79–0.95). Pearson and Spearman correlation analyses quantified parameter–ROP relationships, and the Al-Abduljabbar empirical model, recalibrated via multiple nonlinear regression, served as the evaluation framework. DCM consistently exhibited the strongest negative correlation with ROP across both bit sizes and achieved the highest model accuracy (R² = 0.54, AAPE = 25.33%), significantly outperforming the Bourgoyne and Young model (R² = 0.26, AAPE = 36.55%). A statistically validated scale-dependent effect was identified: Fisher’s Z-transformation tests confirmed that the correlation reversal between CCS and UCS across bit sizes is statistically significant (CCS: Z = −16.84, p < 0.001; UCS: Z = −6.75, p < 0.001), establishing CCS as the superior predictor at 12.25″ and UCS as the superior predictor at 8.5″—a finding not previously reported in the ROP literature. This reversal is attributed to the larger contact area of the 12.25″ bit, which promotes confinement-dominated rock failure better described by CCS, whereas the smaller bit produces localized stress concentration better represented by UCS. These results establish that (1) optimal geomechanical input selection is bit-size dependent, (2) nonlinear modeling outperforms linear frameworks for strength–ROP relationships, and (3) parameter relevance outweighs coefficient tuning in model robustness. DCM is recommended as the most operationally practical universal input, requiring only conventional compressional sonic and density logs. This study provides a systematic framework for geomechanical parameter selection with direct implications for drilling optimization in heterogeneous carbonate reservoirs. Full article

The influence of non-uniform water distributions on the energy characteristics of rock under triaxial stress conditions remains inadequately explored. To investigate the effect of water distributions and cyclic loading conditions on the mechanical properties and energy evolution of sandstone, triaxial cyclic compression tests were conducted on sandstone under varied water immersion heights. The results showed a negative correlation between the water immersion height and peak strength. It was also found that the input strain energy, elastic strain energy, and dissipative strain energy of the samples all followed a quadratic polynomial relationship with stress levels. Interestingly, the linear energy storage, dissipation and damping laws of water-immersed sandstone were confirmed under different water immersion heights. The energy storage, dissipation and damping coefficients nearly remain stable and are independent of the water immersion heights, and the unified linear energy storage and dissipation laws of rock with varying water immersion height were obtained. The findings of this study provide a theoretical basis for the stability evaluation and disaster warning of rock engineering under water-rock interactions. Full article

The current study explored the martensite structures of Fe₃Pt alloys, specifically $Cmmm$-Fe₃Pt, $P{6}_3/mmc$-Fe₃Pt, $P4/mmm$-Fe₃Pt, and $R\overline{3}m$-Fe₃Pt, aiming to provide a comprehensive understanding of the mechanisms that govern their physical and chemical properties. We have focused on their structural, thermodynamical, magnetic, electronic, mechanical, and dynamical characteristics, utilizing the density functional theory (DFT) technique. Our study revealed that in addition to the previously reported austenitic cubic $Pm\overline{3}m$-Fe₃Pt and martensite tetragonal $I4/mmm$-Fe₃Pt with L1₂ structure, there exist additional Fe₃Pt phases that exhibit excellent structural, thermodynamic, magnetic, and mechanical properties. The calculated enthalpies of formation were found to be negative and less than −0.39 eV in all the structures considered, indicating thermodynamic stability and formation under experimental synthetic conditions. Moreover, the computed magnetic moments are in the range 2.94 to 3.04 ${\mathsf{\mu }}_{\mathrm{B}}$, which is relatively comparable to 3.24 ${\mathsf{\mu }}_{\mathrm{B}}$ of the widely reported $Pm\overline{3}m$-Fe₃Pt alloy. The analysis of the electronic structure also revealed strong magnetism due to the presence of asymmetry in the spin-up and -down states of the density of states (DOS) plots. To determine the mechanical response of Fe₃Pt structures under loading conditions, we computed the independent elastic constants, macroscopic properties, and stress–strain relationship under hydrostatic stress. All four phases were studied, but the hypothetical $P{6}_3/mmc$-Fe₃Pt showed excellent mechanical stability at ambient conditions and exceptional hardness and resistance to compression in the elastic region 0% ≤ strain ≤ 10%. This evidence is provided by satisfying the Born necessary stability conditions, large bulk modulus, and a strong linear relationship fit ($R^2$) of greater than 0.94. Moreover, the phonon dispersion curves revealed dynamical stability for $Cmmm$-Fe₃Pt and $R\overline{3}m$-Fe₃Pt, and metastability for $P4/mmm$-Fe₃Pt, while the hypothetical $P{6}_3/mmc$-Fe₃Pt is unstable. Full article

To overcome the physical constraints during the miniaturization of conventional semiconductor devices, spintronics is playing an increasingly prominent role. The Rashba effect, characterized by spin–momentum locking, has emerged as a promising solution to address challenges. Two-dimensional (2D) Janus transition metal dichalcogenides (TMDCs) break spatial inversion symmetry, creating favorable conditions for the Rashba effect. Based on first-principles calculations, 2D Janus materials XMoYZ₂ (X = S/Se/Te; Y = Si/Ge; Z = N/P) were investigated, with strain, external electric field and charge doping employed to modulate the Rashba effect. The strain results reveal that the Rashba constants of XMoYZ₂ increase significantly with compressive strain. Specifically, after applying uniaxial strain, the Rashba constant of TeMoSiP₂ is enhanced to ~2.2 times its initial value. Compressive strain reduces atomic spacing, enhances orbital overlap, and increases spin–orbit coupling (SOC) strength. All the TeMoYZ₂ materials exhibit significant anisotropy under uniaxial strain, which is favorable for spin-oriented transport. SeMoGeP₂ shows an almost linear Rashba constant–electric field correlation, while TeMoGeP₂ and TeMoSiP₂ show non-monotonic variation. The Rashba constant of TeMoSiP₂ can be enhanced to ~2.7 times its intrinsic value under either positive or negative applied electric fields. Charge doping induces negligible changes in the SOC effect. Finally, the optical absorption properties of TeMoGeP₂, TeMoSiN₂, and TeMoSiP₂ were investigated. This study clarifies the mechanism underlying the enhancement of Rashba constants in XMoYZ₂ materials, enriching the research landscape of spintronics. Full article

This study examines the impact of recovered steel fibers (WTSFs) and waste tire aggregates of varying sizes—fine (FWTR), small coarse (SCWTR), and large coarse (LCWTR)—on the compressive strength of concrete subjected to elevated temperatures. Forty mixes were formulated utilizing four distinct WTR replacement ratios (0%, 5%, 10%, 20%) and four WTSF doses (0%, 0.5%, 1%, 2%), and evaluated at temperatures of 24 °C, 100 °C, 200 °C, and 300 °C. The findings indicate that elevated temperatures consistently diminish compressive strength, although the reference concrete saw around 18% loss at 300 °C, with WTR-containing mixes demonstrating losses ranging from 25% to 45%, contingent upon rubber size and dose. The type of WTR was critical—LCWTR mixes exhibited superior residual strength retention due to enhanced particle–matrix interlocking, whereas FWTR mixtures saw the most significant decline. The inclusion of WTSF increased strength by 2–10% at 0.5–1.0% fiber content through crack bridging, but excessive fiber addition (2.0%) decreased workability and caused clustering, leading to up to 40% strength loss. The ideal combination was 5LCWTR–1WTSF, which sustained 36.97 MPa at 24 °C and 29.65 MPa at 300 °C, indicating superior performance across all temperature ranges. Predictive modeling utilizing machine learning techniques (SVR, KRR, 1D-CNN, and DRL) corroborated the experimental results, with the CNN attaining the maximum generalization accuracy (R² = 0.9374) and the KRR exhibiting the most consistent performance (R² = 0.9305). The models indicated that WTR and temperature were the primary variables diminishing strength, although modest WTSF ratios enhanced overall thermal resilience. SHAP and ALE analysis further validated that WTR content exhibited the most significant negative feature contribution (~−6 MPa), succeeded by temperature, although modest fiber inclusion demonstrated a positive SHAP effect (+2–4 MPa), corroborating the experimentally observed non-linear reinforcement threshold. The combined experimental–computational framework demonstrates that the combination of coarse rubber aggregates (5–10%) with appropriate WTSF content (0.5–1.0%) improves sustainability and high-temperature durability. The integration of physical testing and interpretable AI modeling creates a hybrid approach that can anticipate and enhance thermo-mechanical performance in sustainable concrete systems. Full article

We show how a source-aware fluid closure framework for turbulent transport performs well on the confinement timescale in magnetically confined plasmas. A central result is that whether a source is resonant with the turbulence determines which fluid moments must be retained. Using a nonlinear current formulation, we show that resonance broadening—the dominant kinetic nonlinearity—cancels linear resonances and thereby justifies a quasilinear fluid closure already on the turbulence timescale. We derive a practical negative-energy criterion and identify parameter regimes satisfied by ion-temperature-gradient (ITG) modes (slab and toroidal), with parallel ion compressibility and magnetic curvature controlling the sign. The framework clarifies when velocity-space dynamics must be retained in the kinetic Fokker–Planck equation (for example, for fast-particle instabilities at frequencies about 10² higher than drift-wave frequencies). The present study provides additional support for our model by predicting transport that increases with radius and by showing—consistent with nonlinear kinetic simulations—that the diamagnetic flow dominates the Reynolds stress. Altogether, the results obtained provide a consistent, reduced-cost path to fluid closures that retain the essential kinetic physics while remaining tractable on confinement timescales. Full article

Resistance against water penetration is one of the key indicators of concrete durability in humid and pressurized environments. An intelligent model based on the XGBoost machine-learning algorithm was developed to predict the water penetration depth, using 1512 independent experimental measurements. The influential variables included water pressure, pressure duration, thermal cycles, fiber content, curing, and compressive strength. The investigated concrete specimens and field-tested structures in this study were exposed to arid and hot climatic conditions, and the proposed model was developed within this environmental context. To accurately simulate the water transport behavior, a cylindrical-chamber test was employed, enabling non-destructive and in-situ evaluation of structures. Correlation analysis revealed that compressive strength had the strongest negative influence (r = −0.598), while free curing exhibited the strongest positive influence (r = +0.654) on penetration depth. After hyperparameter optimization, the XGBoost model achieved the best performance (R² = 0.956, RMSE = 1.08 mm, MAE = 0.81 mm). Feature importance analysis indicated that penetration volume, pressure, and curing were the most significant predictors. According to the partial dependence analysis, both pressure and duration exhibited an approximately linear increase in penetration depth, while a W/C ratio below 0.45 and curing markedly reduced permeability. Microstructural interpretation using MIP, XRD, and SEM tests supported the physical interpretation of the trends identified by the machine-learning model. The results demonstrate that machine-learning-models can serve as fast and accurate tools for assessing durability and predicting permeability under severe environmental conditions. Finally, the permeability of several real structures was evaluated using the machine-learning approach, showing excellent prediction accuracy. Full article

A comprehensive numerical investigation of Fatigue Crack Growth (FCG) under negative stress ratios (R < 0) was conducted using the Finite Element Method (FEM) and the ANSYS Benchmark 19.2 SMART crack growth module on modified Compact Tension (CT) specimens. This study addresses the critical challenge posed by the compressive portion of cyclic loading, which traditional Linear Elastic Fracture Mechanics (LEFM) models often fail to capture accurately due to the complex interaction of crack closure and reversed plastic zones. The analysis focused on the evolution of the von Mises stress and maximum principal stress distributions at the crack tip across a range of stress ratios, including R = 0.1, −0.1, −0.2, −0.3, −0.4, −0.5, and −1.0. The results demonstrate a significant inverse correlation between fatigue life cycles and the magnitude of the negative stress ratio, consistent with the detrimental effect of increasing tensile stress. Crucially, the numerical simulation successfully captured the non-monotonic behavior of the crack tip stress field, revealing that the compressive load phase substantially alters the effective stress intensity factor range and the crack growth path, which was governed by the Maximum Tangential Stress (MTS) criterion. This research provides a validated computational methodology for accurately predicting FCG life in engineering components subjected to demanding, fully reversed, or compressive–dominant cyclic loading environments. Full article

The rapid development in building construction has stimulated the replacement of cement in concrete with construction waste materials such as marble waste powder (MWP) and granite waste powder (GWP) to reduce the negative impact of cement production and to save natural resources. Therefore, the inclusion of these materials in concrete contributes to environmental sustainability by reducing cement consumption and promoting the reuse of industrial waste. The present study employs Response Surface Methodology (RSM) and, for the first time in a comparable context, the Neural Network Algorithm (NNA) as an advanced optimization and predictive tool to evaluate the synergistic effect of using GWP and MWP as partial cement replacements in concrete exposed to elevated temperatures. The study involved four independent variables: replacement level of GWP up to 9%, replacement level of MWP up to 9%, the degree of temperature (T) up to 800 °C, and the exposure duration (D) up to 2 h, while the dependent variable (output) was the compressive strength (CS). The ANOVA results revealed that the quadratic model outperformed the linear model in predicting the CS of concrete. The Quadratic model, derived from RSM, demonstrated superior performance in predicting CS values. However, the NNA model also showed high predictive accuracy (R² = 0.949; RMSE = 1.5297 MPa), effectively capturing the complex and nonlinear relationships among temperature, duration, and the cement replacement levels with GWP and MWP. The optimization results revealed that the maximum compressive strength of 39.4 MPa can be achieved at 8.92% GWP, 1.89% MWP, T of 247 °C, and D of 0.64 h with a desirability of 1. The proposed models provided valuable insights into the synergistic effects of granite and marble waste powders, supporting the design of sustainable, high-performance concrete with reduced environmental footprint and improved resource efficiency. Full article

During deep coalbed methane (CBM) drilling, wellbore stability is significantly influenced by the interaction between drilling fluid and coal rock. However, quantitative data on mechanical degradation under long-term high-temperature and high-pressure conditions are lacking. This study subjected coal cores to immersion in field-formula drilling fluid at 60 °C and 10.5 MPa for 0–30 days, followed by uniaxial and triaxial compression tests under confining pressures of 0/5/10/20 MPa. The fracture evolution was tracked using micro-indentation (µ-indentation), nuclear magnetic resonance (NMR), and scanning electron microscopy (SEM), establishing a relationship between water absorption and strength. The results indicate a sharp decline in mechanical parameters within the first 5 days, after which they stabilized. Uniaxial compressive strength decreased from 36.85 MPa to 22.0 MPa (−40%), elastic modulus from 1.93 GPa to 1.07 GPa (−44%), cohesion from 14.5 MPa to 5.9 MPa (−59%), and internal friction angle from 24.9° to 19.8° (−20%). Even under 20 MPa confining pressure after 30 days, the strength loss reached 43%. Water absorption increased from 6.1% to 7.9%, showing a linear negative correlation with strength, with the slope increasing from −171 MPa/% (no confining pressure) to −808 MPa/% (20 MPa confining pressure). The matrix elastic modulus remained stable at 3.5–3.9 GPa, and mineral composition remained unchanged, confirming that the degradation was due to hydraulic wedging and lubrication of fractures rather than matrix damage. These quantitative thresholds provide direct evidence for predicting wellbore stability in deep CBM drilling. Full article

The compressive strength (Fc) of cement mortar (CM) is a key parameter in ensuring the mechanical reliability and durability of cement-based materials. Traditional testing methods are labor-intensive, time-consuming, and often lack predictive flexibility. With the increasing adoption of machine learning (ML) in civil engineering, data-driven approaches offer a rapid, cost-effective alternative for forecasting material properties. This study investigates a wide range of supervised linear and nonlinear ML regression models to predict the Fc of CM. The evaluated models include linear regression, ridge regression, lasso regression, decision trees, random forests, gradient boosting, k-nearest neighbors (KNN), and twelve neural network (NN) architectures, developed by combining different optimizers (L-BFGS, Adam, and SGD) with activation functions (tanh, relu, logistic, and identity). Model performance was assessed using the root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Among all models, NN_tanh_lbfgs achieved the best results, with an almost perfect fit in training (R² = 0.9999, RMSE = 0.0083, MAE = 0.0063) and excellent generalization in testing (R² = 0.9946, RMSE = 1.5032, MAE = 1.2545). NN_logistic_lbfgs, gradient boosting, and NN_relu_lbfgs also exhibited high predictive accuracy and robustness. The SHAP analysis revealed that curing age and nano silica/cement ratio (NS/C) positively influence Fc, while porosity has the strongest negative impact. The main novelty of this study lies in the systematic tuning of neural networks via distinct optimizer–activation combinations, and the integration of SHAP for interpretability—bridging the gap between predictive performance and explainability in cementitious materials research. These results confirm the NN_tanh_lbfgs as a highly reliable model for estimating Fc in CM, offering a robust, interpretable, and scalable solution for data-driven strength prediction. Full article