Search Results (7,165)

This study investigates the influence of solidification conditions on the high-cycle fatigue (HCF) behavior of a second-generation DD6 single-crystal superalloy. Single-crystal bars with a [⁰⁰¹] orientation were prepared using the high-rate solidification (HRS) and liquid-metal cooling (LMC) techniques under various pouring temperatures. The HCF performance of the heat-treated alloy was subsequently evaluated at 800 °C using rotary bending fatigue tests. The results demonstrate that increasing the pouring temperature effectively reduced the content and size of microporosity in the HRS alloys. At an identical pouring temperature, the LMC alloy exhibited a significant reduction in microporosity, with its content and maximum pore size being only 44.4% and 45.8% of those in the HRS alloy, respectively. Consequently, the HCF performance was enhanced with increasing pouring temperature for the HRS alloys. The LMC alloy outperformed its HRS counterpart processed at the same temperature, showing a 9.4% increase in the conditional fatigue limit (at 10⁷ cycles). Microporosity was identified as the dominant site for HCF crack initiation at 800 °C. The role of γ/γ′ eutectic in crack initiation diminished or even vanished as the solidification conditions were optimized. Fractographic analysis revealed that the HCF fracture mechanism was quasi-cleavage, independent of the solidification conditions. Under a typical stress amplitude of 550 MPa, the deformation mechanism was characterized by the slip of a/2<011> dislocations within the γ matrix channels, which was also unaffected by the solidification conditions. In conclusion, optimizing solidification conditions, such as by increasing the pouring temperature or employing the LMC process, enhances the HCF performance of the DD6 alloy primarily by refining microporosity, which in turn prolongs the fatigue crack initiation life. Full article

To address the low energy conversion efficiency and weak mechanical strength of In₂O₃ thermoelectric materials for Aiye Processing Equipment, this study systematically investigated the regulatory effects and mechanisms of Ce doping on In₂O₃’s thermoelectric and mechanical properties via experiments. In₂O₃ samples with varying Ce contents were prepared, and property-microstructure correlations were analyzed through electrical/thermal transport tests, Vickers hardness measurements, and crystal structure characterization. Results show Ce doping synergistically optimizes In₂O₃ properties through multiple mechanisms. For thermoelectric performance, Ce⁴⁺ regulates carrier concentration and mobility, enhancing electrical conductivity and power factor. Meanwhile, lattice distortion from Ce-In atomic size differences strengthens phonon scattering, reducing lattice and total thermal conductivity. These effects boost the maximum ZT from 0.055 (pure In₂O₃) to 0.328 at 973 K obtained by x = 0.0065, improving energy conversion efficiency significantly. For mechanical properties, Ce doping enhances Vickers hardness and plastic deformation resistance via solid solution strengthening (lattice distortion hinders dislocations), microstructure densification (reducing vacancies/pores), Ce-O bond strengthening, and defect pinning. This study confirms Ce doping as an effective strategy for simultaneous optimization of In₂O₃’s thermoelectric and mechanical properties, providing experimental/theoretical support for oxide thermoelectric material development and valuable references for their medium-low temperature energy recovery applications. Full article

Background: Visualization (motor imagery) is used in sports to enhance performance. Fencing relies on point control and lunge distance, yet little is known about how visualization affects these skills across experience levels. Objective: To examine the effects of brief visualization on point control and lunge distance in fencers of different experience levels. Methods: Nineteen fencers (age 10–56) completed pre- and post-tests of point control (10 hits) and lunge distance (maximum reach). Between tests, the experimental group performed a 1 min guided visualization, while the control group (n = 20) repeated the tests without visualization. Results: Visualization significantly improved point control (+1.3 hits, 25.5%; p = 0.002). Lunge distance increased (+15.6 cm, 11.1%; p = 0.001). Less experienced fencers improved more in point control (39.0% vs. 14.8%), while experienced fencers improved more in lunge distance (12.8% vs. 7.2%). Control participants showed no meaningful gains, and between-group comparisons confirmed significant advantages for visualization in both skills (p < 0.01). Conclusion: Even a short visualization exercise improved fencing performance, with novices benefiting most in accuracy and experienced fencers in explosive reach. Visualization offers a low-cost, adaptable supplement to fencing training. Full article

In indoor environments, fusion localization methods that combine Wi-Fi fingerprinting and Pedestrian Dead Reckoning (PDR) are constrained by the high sensitivity of traditional filters, such as the Extended Kalman Filter (EKF), to initial states and by their susceptibility to nonlinear drift. This study presents a Wi-Fi/PDR fusion localization approach based on global geometric alignment optimized via a Genetic Algorithm (GA). The proposed method models the PDR trajectory as an integrated geometric entity and performs a global search for the optimal two-dimensional similarity transformation that aligns it with discrete Wi-Fi observations, thereby eliminating dependence on precise initial conditions and mitigating multipath noise. Experiments conducted in a real office environment (14 × 9 m, eight dual-band APs) with a double-L trajectory demonstrate that the proposed GA fusion achieves the lowest mean error of 0.878 m (compared to 2.890 m, 1.277 m, and 1.193 m for Wi-Fi, PDR, and EKF fusion, respectively) and an RMSE of 0.978 m. It also attains the best trajectory fidelity (DTW = 0.390 m, improving by 71.0%, 14.7%, and 27.8%) and the smallest maximum deviation (Hausdorff = 1.904 m, 52.4% lower than Wi-Fi). The cumulative error distribution shows that 90% of GA fusion errors are within 1.5 m, outperforming EKF and PDR. Additional experiments that compare the proposed GA optimizer with Levenberg–Marquardt (LM), particle swarm optimization (PSO), and Procrustes alignment, as well as tests with 30% artificial Wi-Fi outliers, further confirm the robustness of the Huber-based cost and the effectiveness of the global optimization framework. These results indicate that the proposed GA-based fusion method achieves high robustness and accuracy in the tested office-scale scenario and demonstrate its potential as a practical multi-sensor fusion approach for indoor localization. Full article

In plateau and high-altitude areas, freeze-thaw cycles often alter the uniaxial compressive strength (UCS) of rock, thereby impacting the stability of geotechnical engineering. Acquiring rock samples in these areas for UCS testing is often time-consuming and labor-intensive. This study developed a hybrid model based on the XGBoost algorithm to predict the UCS of rock under freeze-thaw conditions. First, a database was created containing longitudinal wave velocity (Vp), rock porosity (P), rock density (D), freezing temperature (T), number of freeze-thaw cycles (FTCs), and UCS. Four swarm intelligence optimization algorithms—artificial bee colony, Newton–Raphson, particle swarm optimization, and dung beetle optimization—were used to optimize the maximum iterations, depth, and learning rate of the XGBoost model, thereby enhancing model accuracy and developing four hybrid models. The four hybrid models were compared to a single XGBoost model and a random forest (RF) model to evaluate overall performance, and the optimal model was selected. The results demonstrate that all hybrid models outperform the single models. The XGBoost model optimized by the sparrow algorithm (R² = 0.94, RMSE = 10.10, MAPE = 0.095, MAE = 7.22) performed best in predicting UCS. SHapley Additive exPlanations (SHAP) were used to assess the marginal contribution of each input variable to the UCS prediction of freeze-thawed rock. This study is expected to provide a reference for predicting the UCS of freeze-thawed rock using machine learning. Full article

This study investigates the Mode II fracture behavior of bamboo–coir–rubber (BCR) hybrid composite panels developed as sustainable alternatives for wood-based panels used in structural applications. The composites were fabricated using alternating bamboo and coir layers within a polypropylene (PP) thermoplastic matrix, with styrene–butadiene rubber (SBR) incorporated as an additive at 0–30 wt.% to enhance interlaminar toughness. Commercial structural plywood was tested as the benchmark. Mode II interlaminar fracture toughness (G_IIc) was evaluated using the ASTM D7905 End-Notched Flexure (ENF) test, supported by optical monitoring to study crack monitoring and Scanning Electron Microscopy (SEM) for microstructural interpretation. Results demonstrated a steady increase in G_IIc from 1.26 kJ/m² for unmodified laminates to a maximum of 1.98 kJ/m² at 30% SBR, representing a 60% improvement over the baseline and nearly double the toughness of plywood (0.7–0.9 kJ/m²). The optimum performance was obtained at 20–25 wt.% SBR, where the laminated retained approximately 85–90% of their initial flexural modulus while exhibiting enhanced energy absorption. Increasing the initial notch ratio (a₀/L) from 0.2 to 0.4 caused a reduction of 20% in G_IIc and a twofold rise in compliance, highlighting the geometric sensitivity of shear fracture to the remaining ligament. Analysis of Variance (ANOVA) confirmed that the increase in G_IIc for the 20–25% SBR laminates relative to plywood and the unmodified composite is significant at p < 0.05. SEM observations revealed rubber-particle cavitation, matrix shear yielding, and coir–fiber bridging as the dominant toughening mechanisms responsible for the transition from abrupt to stable delamination. The measured toughness levels (1.5–2.0 kJ/m²) position the BCR panels within the functional range required for reusable formwork, interior partitions, and transport flooring. The combination of renewable bamboo and coir with a thermoplastic PP matrix and rubber modification hence offers a formaldehyde-free alternative to conventional plywood for shear-dominated applications. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Accurate recognition of complex human activities from wearable sensors plays a critical role in sports analytics and human performance monitoring. However, the high dimensionality and redundancy of raw inertial data can hinder model performance and interpretability. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (MRMR) and Regularized Neighborhood Component Analysis (RNCA) to improve classification accuracy while reducing computational complexity. Multi-sensor inertial data were collected from field hockey players performing six activity types. Time- and frequency-domain features were extracted from four body-mounted inertial measurement units (IMUs), resulting in 432 initial features. MRMR, combined with Pearson correlation filtering (|ρ| > 0.7), eliminated redundant features, and RNCA further refined the subset by learning supervised feature weights. The final model achieved a test accuracy of 92.82% and F1-score of 86.91% using only 83 features, surpassing the MRMR-only configuration and slightly outperforming the full feature set. This performance was supported by reduced training time, improved confusion matrix profiles, and enhanced class separability in PCA and t-SNE visualizations. These results demonstrate the effectiveness of the proposed two-stage feature selection method in optimizing classification performance while enhancing model efficiency and interpretability for real-time human activity recognition systems. Full article

The aim of this study was to investigate differences in the dynamics of ochratoxin A (OTA) production by various Aspergillus isolates under different cultivation conditions. Nine strains representing A. westerdijkiae, A. ochraceus, A. sulphureus, A. carbonarius, and A. albertensis were tested on malt extract agar (MEA), Czapek yeast extract agar (CYA), potato dextrose agar (PDA), and yeast extract sucrose agar (YES). Cultivations were performed at 18 °C, 22 °C, 25 °C, and 30 °C, and OTA production was monitored on the 6th, 10th, 14th, 21st, and 30th days using HPLC analysis. OTA yields strongly depended on the producing strain, with significant variability even among isolates of the same species. The most productive strain was A. ochraceus from cereals with a maximum concentration of 848 µg g⁻¹ OTA, followed by two isolates of A. westerdijkiae from grapes of Slovak origin (591 and 479 µg g⁻¹), and A. sulphureus from soil (546 µg g⁻¹). In contrast, A. carbonarius strains showed the weakest OTA production. Across media, YES supported the highest toxin levels, whereas the most favourable cultivation temperatures were 18 °C and 25 °C. Each strain reached its production maximum at different time points, highlighting the strain-specific nature of OTA biosynthesis. Full article

This study investigates the effect of electrolytic-plasma hardening time on the microstructure formation, hardness distribution, and corrosion behavior of grade 45 structural steel. The treatment was performed in a 15% aqueous sodium carbonate (Na₂CO₃) solution at an applied voltage of 300 V for different holding times (8, 10, and 12 s). Scanning electron microscopy and X-ray diffraction analyses revealed that increasing the EPH duration promotes the formation of a more uniform martensitic layer and reduces the amount of residual cementite. Microhardness measurements showed an increase in surface hardness from 190 HV for the untreated steel to 770 HV after the longest treatment. The cross-sectional hardness profile indicated the presence of a thin decarburized sublayer and a zone of maximum hardness corresponding to the martensitic structure. Potentiodynamic polarization tests in a 0.5 M NaCl solution showed a slight increase in corrosion current density after treatment; however, the corrosion rate remained within the range of 0.19–0.45 mm year⁻¹, confirming the satisfactory corrosion resistance of the hardened layer. The results demonstrate that controlling the EPH duration allows for optimizing the balance between enhanced hardness and maintained corrosion resistance of grade 45 steel. Full article

Generalized censoring, combined with a power-based distribution, improves inferential efficiency by capturing more detailed failure-time information in complex testing scenarios. Conventional censoring schemes may discard substantial failure-time information, leading to inefficiencies in parameter estimation and reliability prediction. To address this limitation, we develop a comprehensive inferential framework for the alpha-power Weibull (APW) distribution under a generalized progressive hybrid Type-II censoring scheme, a flexible design that unifies classical, hybrid, and progressive censoring while guaranteeing test completion within preassigned limits. Both maximum likelihood and Bayesian estimation procedures are derived for the model parameters, reliability function, and hazard rate. Associated uncertainty quantification is provided through asymptotic confidence intervals (normal and log-normal approximations) and Bayesian credible intervals obtained via Markov chain Monte Carlo (MCMC) methods with independent gamma priors. In addition, we propose optimal censoring designs based on trace, determinant, and quantile-variance criteria to maximize inferential efficiency at the design stage. Extensive Monte Carlo simulations, assessed using four precision measures, demonstrate that the Bayesian MCMC estimators consistently outperform their frequentist counterparts in terms of bias, mean squared error, robustness, and interval coverage across a wide range of censoring levels and prior settings. Finally, the proposed methodology is validated using real-life datasets from engineering (electronic devices), clinical (organ transplant), and physical (rare metals) studies, demonstrating the APW model’s superior goodness-of-fit, reliability prediction, and inferential stability. Overall, this study demonstrates that combining generalized censoring with the APW distribution substantially enhances inferential efficiency and predictive performance, offering a robust and versatile tool for complex life-testing experiments across multiple scientific domains. Full article

Additive manufacturing has increasingly become a transformative approach in the design and fabrication of personalized medical devices, offering improved adaptability, reduced production time, and enhanced patient-specific functionality. Within this framework, simulation-driven design plays a critical role in ensuring the structural reliability and performance of orthopedic supports before fabrication. This research study delineates the novel development of a wrist hybrid splint (WHS) which has a simulation-based design and was additively manufactured using fused deposition modeling (FDM). The primary material selected for this purpose was polylactic acid (PLA), recognized for its biocompatibility and structural integrity in medical applications. Prior to the commencement of the actual FDM process, an extensive pre-analysis was imperative, involving the application of nonlinear numerical models aiming at replicating the mechanical response of the WHS in respect to different deposition configurations. The methodology encompassed the evaluation of a sophisticated material model incorporating a damage mechanism which was grounded in experimental data derived from meticulous tensile and three-point bending testing of samples with varying FDM process parameters, namely nozzle diameter, layer thickness, and deposition orientation. The integration of custom subroutines with utility routines was coded with a particular emphasis on maximum stress thresholds to ensure the fidelity and reliability of the simulation outputs on small scale samples in terms of their elasticity and strength. After the formulation and validation of these computational models, a comprehensive simulation of a full-scale, finite element (FE) model of two WHS design variations was conducted, the results of which were aligned with the stringent requirements set forth by the product specifications, ensuring comfortable and safe usage. Based on the results of this study, the final force comparison between the numerical simulation and experimental measurements demonstrated a discrepancy of less than 2%. This high level of agreement highlights the accuracy of the employed methodologies and validates the effectiveness of the WHS simulation and fabrication approach. The research also concludes with a strong affirmation of the material model with a damage mechanism, substantiating its applicability and effectiveness in future manufacturing of the WHS, as well as other orthopedic support devices through an appropriate selection of FDM parameters. Full article

Humidity monitoring is essential in industrial and scientific scenarios, yet remains challenging for compact EMI (electromagnetic interference)-immune sensors with high sensitivity and robust stability. A novel fiber Bragg grating (FBG) humidity sensor was developed, which incorporated LiCl@UIO-66 microfillers within a poly(N-isopropylacrylamide) (PNIPAM) hydrogel matrix. Structural characterization using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy confirms that LiCl is confined or nanodispersed within intact UIO-66, and that interfacial ion–dipole/hydrogen-bonding exists between the composite and water. Systematic variation in coating time (30–720 min) reveals monotonic growth of the total wavelength shift with diminishing returns. A coating time of 4 h was found to yield a wavelength shift of approximately 0.38–0.40 nm, representing about 82% of the maximum shift observed at 12 h, while maintaining good quasi-linearity and favorable kinetics. Calibration demonstrates sensitivities of 6.7 pm/%RH for LiCl@UIO-66_33 and 10.6 pm/%RH for LiCl@UIO-66_51 over ~0–95%RH. Stepwise tests show response times t90 of ≈14 min for both composites, versus ≈30 min for UIO-66 and ≈55 min for neat PNIPAM. Long-term measurements on the 51 wt.% device are stable over the first ~20 days, with only slow drift thereafter, and repeated humidity cycling is reversible. The wavelength decreases monotonically during drying while settling time increases toward low RH. The synergy of hydrogel–MOF–salt underpins high sensitivity, accelerated transport, and practical stability, offering a scalable route to high-performance optical humidity sensing. Full article

In this study, titanium alloy-based composites reinforced with carbon nanotubes (CNTs) and graphene (Gr) were fabricated via spark plasma sintering (SPS). The effects of CNT and Gr reinforcements on the microstructure, density, hardness, and tribological properties of the composites were systematically investigated. The results revealed that CNTs and Gr were dispersed within the Ti alloy matrix. All composites exhibited high relative densities about 99%, confirming the strong densification capability of the SPS process. The incorporation of CNTs and Gr significantly enhanced the mechanical performance of the composites. The maximum hardness values of 445.8 HV and 430.5 HV were obtained for CNT/Ti and Gr/Ti composites containing 3 vol.% reinforcement, corresponding to improvements of 34% and 30%, respectively, compared with the unreinforced Ti alloy. Tribological tests further revealed notable reductions in the coefficient of friction and wear rate for both CNT/Ti and Gr/Ti composites. These enhancements are attributed to the formation of a lubricating tribo-film composed of carbonaceous species and oxide particles (TiO₂, Al₂O₃) on the worn surfaces. Among the two reinforcements, the obtained results indicated that CNTs are more effective in enhancing hardness, whereas graphene provides superior improvement in wear resistance of Ti alloy-based composites. Overall, this work demonstrated that the combination of Ti alloys with nanocarbon reinforcements is an effective approach to simultaneously enhance their mechanical and tribological performance. Full article

In this study, a battery case was developed using a 3D (three dimensional)-printed composite of hexagonal boron nitride (hBN) and polylactic acid (PLA) to enhance the thermal performance of lithium-ion battery (LiB) modules. A 10 wt.% amount of hBN was incorporated into the PLA matrix to improve the composite’s thermal conductivity while maintaining electrical insulation. A 3S2P (3 series and 2 parallel) battery configuration was initially evaluated based on the results of a baseline study for comparison and subsequently subjected to a newly developed test procedure to assess the thermal behavior of the designed case under identical environmental conditions. Initially, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were utilized for material characterization, and their results verified the successful integration of hBN by confirming its presence in the hBN-PLA composite. In thermal tests, experimental results revealed that the fabricated hBN-PLA composite battery case significantly enhanced heat conduction and reduced surface temperature gradients compared to the previous baseline study with no case. Specifically, the maximum cell temperature (T_max) decreased from 48.54 °C to 45.84 °C, and the temperature difference (ΔT) between the hottest and coldest cells was reduced from 4.65 °C to 3.75 °C, corresponding to an improvement of approximately 20%. A 3S2P LiB module was also tested under identical environmental conditions using a multi-cycle charge–discharge procedure designed to replicate real electric vehicle (EV) operation. Each cycle consisted of sequential low and high discharge zones with gradually increased current values from 2 A to 14 A followed by controlled charging and rest intervals. During the experimental procedure, the average ΔT between the cells was recorded as 2.38 °C, with a maximum value of 3.50 °C. These results collectively demonstrate that the 3D-printed hBN-PLA composite provides an effective and lightweight passive cooling solution for improving the thermal stability and safety of LiB modules in EV applications. Full article