Search Results (106)

The morphology and distribution of intermetallic compounds (IMCs), such as Al₆Mn, Al₂Cu, and Al₁₂Fe₃Si₂, play a critical role in determining the mechanical properties of 3xxx series aluminum alloys. In this study, the compressive behavior of these IMCs was systematically investigated using the modified embedded atom method (MEAM) potential and the large-scale atomic/molecular massively parallel simulator (LAMMPS) under various temperatures and strain rates. The results show that as the temperature increases from 623 K to 823 K, both the compressive strength and elastic modulus of the IMCs decrease significantly. Al₁₂Fe₃Si₂ exhibits the lowest compressive strength, ranging from 1.1 to 9.8 GPa, while Al₂Cu demonstrates the highest compressive strength, ranging from 3.9 to 19.8 GPa. Within this temperature range, Al₆Mn and Al₃Fe show relatively poor stability. At a strain rate of 1 × 10¹⁰ s⁻¹, the thermal sensitivity coefficients for compressive strength are 0.010 and 0.008, and those for elastic modulus are 0.173 and 0.126, respectively. In contrast, Al₂Cu exhibits the best stability, with thermal sensitivity coefficients of 0.005 for compressive strength and 0.041 for elastic modulus. Furthermore, the influence of strain rate diminishes notably under lower temperatures. Across the entire temperature range, Al₂Cu displays the highest overall stability, with a strain rate sensitivity index ranging from 0.3527 to 0.3738. Full article

Poly(lactic acid) (PLA) is the most widely used feedstock in material extrusion (MEX) 3D printing. In this study, PLA was combined with 0–40 wt.% of poly(butylene adipate-co-terephtalate) (PBAT) to improve its ductility. The resulting blends were processed into filaments suitable for MEX 3D printing and used to fabricate specimens for mechanical characterization using three distinct raster angles (RAs; 0°, ±45°, and 90°) to statistically evaluate the individual and joint effects of blend composition and raster orientation. Melt flow index (MFI) measurements showed that increasing PBAT content reduced the MFI from 40.4 g/10 min to 34.4 g/10 min, which led to weaker bonding between printed beads, as shown in scanning electron microscopic images. Tensile strength, modulus, and impact strength were evaluated using tensile and Charpy tests. Statistical analysis showed that RA, PBAT concentration, and their interaction all significantly influenced (p < 0.05) mechanical performance. Both strength and modulus decreased as PBAT content and RA increased, with the highest values of 50 MPa and 2.78 GPa observed for neat PLA 3D-printed at 0° RA, and the lowest values of 15 MPa and 1.05 GPa for 40 wt.% PBAT at 90° RA. In contrast, incorporating PBAT improved impact strength, showing its toughening effect. Meanwhile, no clear trend between impact resistance and RA was observed. The highest impact strength (52.7 kJ/m²) was found at 40 wt.% PBAT content and ±45° RA. Full article

Optical pressure sensors offer the advantages of high sensitivity, immunity to interference, and suitability for use in extreme environments. Based on the defect-immune, unidirectional transmission characteristics of valley photonic crystals (VPCs) and the refractive-index modulation of germanium under different pressures, we designed a topological ring resonator pressure sensor based on germanium VPCs. The shift of the resonance peak in the optical communication wavelength range with respect to pressure magnitude is studied to realize a pressure-sensing function. The results show that within the range of 0–10 GPa, the wavelength of the single resonance peak of the topological ring resonator pressure sensor shifts from 1580 nm to 1489 nm as the pressure increases. The sensor’s maximum detection sensitivity is 24.34 nm/GPa, and the transmittance across the bandwidth remains consistently above 0.85, with a maximum of 0.97. The germanium-based topological ring resonator pressure sensor features a compact structure with a size of 7.5 μm × 6.5 μm. It can be manufactured using existing nanofabrication technology and will have broad application prospects in the field of integrated photonic chips. Full article

High-performance spun bionanocomposite fibers, composed of high-molecular-weight chitosan (HMW), low-molecular-weight chitosan “oligomers” (LMW), and cellulose nanofibers (CNFs), were successfully fabricated via gel spinning of viscous aqueous chitosan (CHI) based formulations into a NaOH coagulation bath. The X-ray diffraction (XRD) analysis revealed that the incorporation of cellulose nanofibers contributed to enhance crystallinity of chitosan in spun fibers. The spinning process, which comprised sequential acidic solubilization, basic neutralization, stretching, and drying steps, produced chitosan/CNF composite fibers with high crystallinity, further enhanced by the incorporation of low molecular weight chitosan. The cellulose nanofibers seem to promote CHI crystallization, by acting as nucleation sites for the nucleation and growth of chitosan crystals, with those latter of LMW further enhancing crystallization and orientation due to higher mobility of shorter polymer chains. Two-dimensional XRD patterns demonstrated the preferential alignment of both CNFs and chitosan crystals along the fiber axis. Increasing the proportion of short-chain chitosan led to a reduction of the viscosity of collodion, facilitating the spinning of solutions with higher polymer concentrations. The X-ray diffraction (XRD) analysis revealed that the addition of low-molecular-weight chitosan (LMW), with an intermediate molecular weight Mw of ~4.4 × 10⁴ g/mol, produced the most significant improvements in the crystallinity index (CrI) and orientation. This structural enhancement corresponded to superior mechanical properties like Young’s modulus, yield stress σ_y, and stress-at-break σ_b of the processed composite fibers. By incorporating that intermediate molecular weight chitosan, a Young’s modulus as high as 20 GPa was achieved for the spun composite fibers, which was twice higher than the modulus of around 10 GPa obtained by adding the lowest molecular weight chitosan of Mw ~ 2.9 × 10⁴ g/mol in the composite, and largely above the modulus of around 5 GPa obtained for fiber just spun with chitosan without incorporation of cellulose nanofibers. Full article

Hard Cr-Al-Si-B-(N) coatings were deposited in Ar and Ar–15%N₂ medium by d.c. magnetron sputtering of composite targets manufactured using self-propagating high-temperature synthesis. The structure of the coatings was studied by X-ray diffraction, scanning and transmission electron microscopy, energy dispersion spectroscopy, and glow discharge optical emission spectroscopy. The coating properties were determined by nanoindentation, scratch testing, and tribological pin-on-disc testing at room and elevated temperatures. The oxidation resistance and diffusion barrier properties of the coatings were also evaluated. The results obtained showed that non-reactive coatings had a coarse crystalline structure and contained Cr₅Si₃, CrB_x, and Cr₂Al phases. The introduction of nitrogen into the coating composition promoted crystallite refinement and structural amorphization. Non-reactive CrAl₄Si₁₁B₂₁ coatings had a maximum hardness up to 29 GPa and an elastic modulus up to 365 GPa. The introduction of nitrogen into the coating composition resulted in a 16–32% reduction in mechanical properties. The CrAl₆Si₁₂B₅N₂₅ coating, which exhibited maximal plasticity index H/E = 0.100 and resistance to plastic deformation H³/E² = 0.247 GPa, was characterized by a minimum wear rate Vw = 5.7 × 10⁻⁶ mm³N⁻¹m⁻¹ and a friction coefficient of 0.47. While the CrAl₁₈Si₁₁B₅N₂₆ coating demonstrated a record level of oxidation resistance and successfully resisted oxidation up to a temperature of 1300 °C. Full article

Background: While diabetes is a recognized risk factor for asthma, the shared genetic components between diabetes/glycemic traits and asthma remain unclear. This study investigates the genetic associations, causal relationships, and underlying mechanisms linking these conditions. Methods: We assessed global genetic correlations using linkage disequilibrium score regression (LDSC), high-definition likelihood analysis (HDL), and genetic covariance analysis (GNOVA). Trait pairs with significant correlations subsequently underwent genetic overlap analysis (Genetic analysis integrating Pleiotropy and functional Annotation, GPA) and local genetic correlation analysis (Local Genetic Variant Association Analysis, LAVA). Cross-phenotype association (CPASSOC) and multitrait analysis of GWAS (MTAG) identified potential pleiotropic loci, followed by colocalization and functional annotation. Proteome-wide association study (PWAS) revealed proteins and pathways shared between diabetes/glycemic traits and asthma. Generalized summary-data-based Mendelian randomization (GSMR) was used to evaluate causal effects between diabetes/glycemic traits and asthma. Results: Significant genetic correlations were observed between body mass index (BMI) and asthma (rg = 0.280–0.397; FDR < 0.05), type 2 diabetes mellitus (T2DM) and asthma (rg = 0.240–0.289; FDR < 0.05) across all three methods. GPA revealed significant genome-wide genetic overlap, highest for BMI and asthma (pleiotropy association ratio [PAR] = 0.377) and T2DM-asthma (PAR = 0.353). LAVA identified 111 significant local correlation regions, predominantly between T2DM and asthma (70 regions). Colocalization analysis identified 24 shared pleiotropic loci, predominantly on chromosome 8. Local genetic correlation analysis revealed extensive correlations between T2DM and asthma. PWAS identified 46 shared proteins, with IL6R, MAPK3, and CSF2 being key hubs. Protein–protein interaction analysis highlighted enrichment in JAK-STAT signaling, Th1/Th2 differentiation, and IL-17 pathways. GSMR demonstrated causal effects of BMI (OR = 1.47, 95% CI: 1.42–1.53, FDR < 0.05) and T2DM (OR = 1.06, 95% CI: 1.04–1.08, FDR < 0.05) on increased asthma risk, with no evidence of reverse causality. Conclusions: Obesity (BMI) and T2DM exert causal effects on asthma risk via shared genetic loci and inflammatory pathways, particularly involving IL6R, MAPK3, CSF2, and JAK-STAT signaling. Targeting these colocalized proteins may offer potential therapeutic strategies. Full article

The single-crystal diamond (SCD) possessing both favorable dielectric properties and low stress is esteemed as the ideal material for terahertz windows. The intrinsic step-like growth pattern of SCD can easily lead to stress concentration and a decrease in dielectric performance. In this study, a “two-step method” was designed to optimize the growth mode of SCD. A novel large platform growth pattern has been achieved by controlling diamond seed crystal etching and the epitaxial layer growth process. The experimental results indicate that, compared with the traditional step-like growth model, the root mean square (RMS) roughness of as-prepared SCD reduced from 5 nanometers (step growth) to 0.4~1.0 nanometers (platform growth) within a 5 μm × 5 μm area. Furthermore, the growth step height difference diminished from 30 nm to 3~4 nm, thereby mitigating stress induced by steps to a mere 0.1976 GPa. Additionally, at frequencies ranging from 0.1 to 3 THz, the diamond windows exhibit lower refractive index, dielectric constant, and dielectric loss. Finally, large platform growth effectively reduces phenomena such as dislocation pile-up brought about by step growth, achieving low-damage ultra-precision machining of diamond windows measuring 1 mm in diameter. Full article

When jointed rock masses are in a high-stress environment, the roughness of the joints is the key factor controlling their shear strength. Their loading behavior is also different from the constant normal load (CNL) conditions controlled in conventional laboratories; rather, they follow the constant normal stiffness (CNS) conditions. To investigate the effects of normal stiffness and roughness on the shear mechanical properties of unfilled joint surfaces, shear tests were simulated using PFC3D (5.0) software under CNS conditions. The effects of normal stiffness of 0 (constant normal stress of 4 MPa), 0.028 GPa/m (low normal stiffness), 0.28 GPa/m (medium normal stiffness), and 2.8 GPa/m (high normal stiffness), and joint roughness coefficients (JRC) of 2~4 (low roughness), 10~12 (medium roughness), and 18~20 (high roughness) on the shear stress, normal stress, normal deformation, surface resistance index, and block failure characteristics of the joint surface were obtained. The results indicate that for different combinations of normal stiffness—JRC—the shear simulation process primarily exhibits three deformation stages: linear stage, yield stage, and post-peak stage. Shear stress increases initially and then decreases as shear displacement increases. When normal stiffness is no less than 0.28 GPa/m, both normal stress and JRC increase gradually with increasing JRC and normal stiffness. When the normal stiffness is no greater than 0.028 GPa/m, the normal stress shows no significant change. The normal displacement changes from “shear contraction” to “shear expansion” with increasing shear displacement and from positive to negative values while the displacement gradually increases; the maximum normal displacement decreases with increasing normal stiffness and increases with increasing JRC. The peak SRI value increases with increasing JRC and decreases with increasing normal stiffness. As normal stiffness increases, the number of tensile cracks for JRC 2~4 first decreases and then increases, while the number of shear cracks gradually increases; for JRC 10~12 and 18~20, both the number of shear cracks and tensile cracks increase with increasing normal stiffness. This paper simulates the actual mechanical environment of deep underground joints to expound the influence of normal stiffness and joint roughness on the stability of deep rock masses. The research results can provide certain theoretical references for predicting the stability of deep surrounding rocks and the stress of support structures. Full article

The COVID-19 pandemic increased mental health issues and heavily affected the academic performance of college students. The study aimed to assess the association of psychological health and behavioral factors with academic performance among undergraduate students during the third year of the COVID-19 pandemic. The study was conducted at a small liberal arts undergraduate college in rural Appalachia. A cross-sectional research design was followed. Data was collected online using Qualtrics in person in July 2021. Participants were selected randomly. World Health Organization-5, Perceived Stress Scale 4, The Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7, and Patient Health Questionnaire scales for well-being, stress, sleep quality, anxiety, and depression were used, respectively. Spearman’s correlation, t-test, analysis of variance, and multiple regression were conducted. Overall well-being, perceived stress, generalized anxiety, depression, and sleep quality were found to be significantly different by gender and exercise. The Grade Point Average (GPA) was found to be negatively associated with depression and positively associated with Sleep quality. Findings suggest that students who have scored higher on the depression and sleep quality scale may be affected more during this COVID-19 pandemic in maintaining a good GPA. The findings of this study can help generate hypotheses for further research and guide interventions to address poor academic performance. Full article

γ-TiAl alloy is a lightweight high-temperature structural material, featuring low density, excellent high-temperature strength, creep resistance, etc. It is a key material in the aerospace field. However, the essential defects of γ-TiAl alloys, such as poor room-temperature plasticity and low fracture toughness, have become the biggest obstacles to their practical application. Therefore, in this paper, the physical mechanism of modification of the mechanical properties and electronic structure of γ-TiAl alloys by doping with Sc, V, and Si was investigated by using the first-principles pseudopotential plane wave method. This paper specifically calculates the geometric structure, phonon spectrum, mechanical properties, electron density of states, Mulliken population analysis, and differential charge density of γ-TiAl alloys before and after doping. The results show that after doping, the structural parameters of γ-TiAl have changed significantly, and the doping models all have thermodynamic stability. The B, G, and E values of the doped system are, respectively, within the range of 94–112, 57–69, and 143–170 GPa, indicating that the material’s ability to resist compressive deformation is weakened. Moreover, the B/G values change from 1.5287 to 1.6350, 1.7279, and 1.6327, respectively, and a transformation from brittleness to plasticity occurs. However, it is still lower than the critical value of 1.75, indicating that the doped γ-TiAl alloy material retains its high-strength characteristics while also exhibiting a certain degree of toughness. The total elastic anisotropy index of the doped system increases, and the degree of anisotropy of mechanical behavior significantly increases. The total electron density of states diagram indicates that γ-TiAl alloys possess conductive properties. The covalent interactions between doped atoms and adjacent atoms have been weakened to varying degrees, which is manifested as a significant change in the charge distribution around each atom. The above results indicate that the doping of Sc, V, and Si can effectively tune the mechanical properties and electronic structure of γ-TiAl alloys. Full article

Background: Nursing students usually face excessive academic and clinical demands that negatively impact their sleep quality. Physical activity (PA) has been proposed to enhance sleep, yet few investigations have focused on this correlation within the Saudi environment. The purpose of this research was to determine the relationship between PA and sleep quality in Saudi nursing students. Methodology: A cross-sectional study was conducted among a sample of 554 nursing students from different universities in Saudi Arabia. The International Physical Activity Questionnaire–Short Form (IPAQ-SF) and the Pittsburgh Sleep Quality Index (PSQI) were used to collect data. Snowball sampling was used to recruit the participants through social media. Descriptive statistics, Pearson correlation, and inferential tests were employed for the analysis. Results: Most participants were female (85.1%) and aged 18–34 years (95.5%). LPA levels were reported by 59.6% of students, and only 8.2% engaged in VPA. The majority (91%) reported poor sleep duration (<5 h), and 57.4% had poor sleep efficiency. Overall, 86.7% of students experienced poor sleep quality. Gender and GPA were significantly associated with both PA and sleep quality. Female students and those with lower GPAs were more likely to report LPA and poor sleep. Marital status was also associated with higher levels of VPA. No significant associations were found with age, academic year, region, income, or parental education. A statistically significant positive correlation was found between PA and sleep quality (r = 0.192, p < 0.001), suggesting that increased PA is modestly associated with better sleep. Conclusion: The research shows a high rate of poor sleep and low physical activity in nursing students, indicating an alarming health trend. Although PA was linked significantly to better sleep, the modest strength indicates the necessity for multi-component interventions. Structured PA programs and sleep hygiene education should be incorporated into nursing curricula by universities to foster students’ well-being and academic performance. Full article

Background/Objectives: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic subgroups of non-small-cell lung cancer (NSCLC) patients with brain metastasis (BM). Simple-yet-effective algorithms designed to identify similar group characteristics will assist clinicians in categorizing patients effectively. Methods: We retrospectively collected data from 95 NSCLC patients with BM treated at two oncology centers. To identify clinically distinct subgroups, two types of unsupervised clustering methods—two-step clustering (TSC) and hierarchical cluster analysis (HCA)—were applied to the baseline clinical data. Patients were categorized into prognostic classes according to the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA). Survival curves for the clusters and DS-GPA classes were generated using Kaplan–Meier analysis, and the differences were assessed with the log-rank test. The discriminative ability of three categorical variables on survival was compared using the concordance index (C-index). Results: The mean age of the patients was 61.8 ± 0.9 years, and the majority (77.9%) were men. Extracranial metastasis was present in 71.6% of the patients, with most (63.2%) having a single BM. The DS-GPA classification significantly divided the patients into prognostic classes (p < 0.001). Furthermore, statistical significance was observed between clusters created by TSC (p < 0.001) and HCA (p < 0.001). HCA showed the highest discriminatory power (C-index = 0.721), followed by the DS-GPA (C-index = 0.709) and TSC (C-index = 0.650). Conclusions: Our findings demonstrated that the TSC and HCA models were comparable in prognostic performance to the DS-GPA index in NSCLC patients with BM. These results suggest that unsupervised clustering may offer a data-driven perspective on patient stratification, though further validation is needed to clarify its role in prognostic modeling. Full article

It is vital to stabilize pillar dams in underground reservoirs in coal mine goafs to protect groundwater resources and quarry safety, practice green mining, and protect the ecological environment. Considering the actual occurrence of coal pillar dams in underground reservoirs, acoustic emission (AE) mechanical tests were performed on dry, naturally absorbed, and soaked coal samples. According to the mechanical analysis, Quantitative analysis revealed that dry samples exhibited the highest mechanical parameters (peak strength: 12.3 ± 0.8 MPa; elastic modulus: 1.45 ± 0.12 GPa), followed by natural absorption (peak strength: 9.7 ± 0.6 MPa; elastic modulus: 1.02 ± 0.09 GPa), and soaked absorption showed the lowest values (peak strength: 7.2 ± 0.5 MPa; elastic modulus: 0.78 ± 0.07 GPa). The rate of mechanical deterioration increased by ~25% per 1% increase in moisture content. It was identified that the internal crack development presented a macrofracture surface initiating at the sample center and expanding radially outward, and gradually expanding to the edges by adopting AE seismic source localization and the K-means clustering algorithm. Soaked absorption was easier to produce shear cracks than natural absorption, and a higher water content increased the likelihood. The b-value of the AE damage evaluation index based on crack development was negatively correlated with the rock damage state, and the S-value was positively correlated, and both effectively characterized it. The research results can offer reference and guidance for the support design, monitoring, and warning of coal pillar dams in underground reservoirs. (The samples were tested under two moisture conditions: (1) ‘Soaked absorption’—samples fully saturated by immersion in water for 24 h, and (2) ‘Natural absorption’—samples equilibrated at 50% relative humidity and 25 °C for 7 days). Full article

Background: Bone elasticity is one of the most important biomechanical parameters of the skeleton. It varies markedly with age, anatomical zone, bone type (cortical or trabecular) and bone marrow status. Methods: This review presents the result of a systematic review and analysis of 495 experimental and analytical papers on the elastic properties of bone tissue. The bone characteristics of hip, shoulder, skull, vertebrae as a function of the factors of age (young and old), sex (male and female), presence/absence of bone marrow and different test methods are examined. The Bayesian neural network (BNN) was used to estimate the uncertainty in some skeletal parameters (age, sex, and body mass index) in predicting bone elastic modulus. Results: It was found that the modulus of elasticity of cortical bone in young people is in the range of 10–30 GPa (depending on the type of bone), and with increasing age, this slightly decreases to 10–25 GPa, while trabecular tissue varies from 0.2 to 5 GPa and reacts more acutely to osteoporosis. Bone marrow, according to several studies, is able to partially increase stiffness under impact loading, but its contribution is minimal under slow deformations. Conclusions: BNN confirmed high variability, supplementing the predictions with confidence intervals and allowed the formation of equations for the calculation of bone tissue elastic modulus for the subsequent selection of the recommended elastic modulus of the finished implant, taking into account the biomechanical characteristics of bone tissue depending on age (young and old), sex (men and women) and anatomical zones of the human skeleton. Full article

The reflectance (R) of linear and circular micro-gratings on c-plane sapphire Al₂O₃ ablated by a femtosecond (fs) laser were spectrally characterised for thermal emission $\propto (1-R)$ in the mid-to-far infrared (IR) spectral range. An IR camera was used to determine the blackbody radiation temperature from laser-patterned regions, which showed (3–6)% larger emissivity dependent on the grating pattern. The azimuthal emission curve closely followed the Lambertian angular profile $\propto cos{\theta }_a$ at the 7.5–13 μm emission band. The back-side ablation method on transparent substrates was employed to prevent debris formation during energy deposition as it applies a forward pressure of >0.3 GPa to the debris and molten skin layer. The back-side ablation maximises energy deposition at the exit interface where the transition occurs from the high-to-low refractive index. Phononic absorption in the Reststrahlen region 20–30 μm can be tailored with the fs laser inscription of sensor structures/gratings. Full article