Search Results (244)

Background/Objectives: Denture disinfection is a crucial step in reducing microbial colonization and the risk of denture stomatitis, as well as contributing to patient health and denture longevity; thus, it was obligatory to select an effective cleanser without undesirable impact on properties of acrylic denture base material. This study aimed to assess the synergistic effect of white vinegar and sodium bicarbonate (WVSB) mixture on Candida albicans by means of colony forming unit (CFU) and adhesion assays, as well as the surface roughness and flexural strength of heat-cured acrylic denture base material after being immersed in the WVSB mixture. Methods: In total, 200 specimens of heat-cured acrylic resin were prepared: 50 per each test, 5 per each group. They were divided into ten groups; distilled water (negative control), a Corega denture cleanser tablet soaked for 5 min (positive control), and four concentrations (2%, 3%, 4% and 5%) of WVSB mixture were made and examined for (5 and 10 min) immersion durations. Statistical analysis was performed by using Welch’s ANOVA alongside Games–Howell post hoc tests for CFU assay and one-way ANOVA along with Tukey HSD post hoc tests for remaining tests. A p < 0.05 was considered significant in all experiments. Results: The results for the CFU, adhesion and surface roughness tests showed that the WVSB mixture demonstrated a statistically significant difference in most test groups compared to the negative control group, while the flexural strength test showed a statistically non-significant difference. Conclusions: The WVSB mixture showed concentration and time-dependent antifungal effects against C. albicans, with increased surface roughness and no negative effect on the flexural strength of heat-cure acrylic. Full article

Apis cerana (A. cerana) is a native and widely managed honey bee species in China. Body size and body weight are crucial breeding traits, as colonies possessing individuals with large body weight tend to be healthier and exhibit high productivity. This study aimed to clarify the relationships between body size and body weight in A. cerana and to evaluate their associations with geographic, climatic, and colony productive traits for selective breeding. Body size and body weight were measured in virgin queens, drones, and workers from Jinfo Mountain, Chongqing, and additional measurements of queens and drones were implemented in five other regions across China. Linear mixed-effects models confirmed that body size had a significant positive effect on body weight in virgin queens, drones, and workers. However, correlations of body-size and body-weight traits among different bee groups were weak and non-significant after FDR correction, indicating that drones or workers cannot be used as direct substitutes for queen body-size traits in the present dataset. Standardized model estimates showed that queen and drone body-size and body-weight traits were consistently negatively associated with annual minimum and annual mean temperatures, but positively associated with latitude after FDR adjustment. Annual precipitation was also negatively associated with queens’ body size, queens’ body weight, and drones’ body size, whereas annual maximum temperature, longitude, and elevation showed no significant associations after FDR adjustment. Moreover, queens’ body size and body weight were significantly positively associated with honey yield, honey yield during the main nectar flow, and colony gentleness after FDR correction, whereas their associations with the number of effective eggs laid by queens, colony strength, and robbery were not significant after FDR correction. These findings suggest that queen body-type traits may serve as useful auxiliary indicators for selecting colonies with higher honey production and gentler behavior, but their relationships with other colony traits should be interpreted cautiously. This research is beneficial for initiating a body size-weight selective breeding program for A. cerana, as it can help optimize breeding objectives and accelerate genetic progress. Full article

Ti-6Al-4V thin-walled specimens were fabricated by gas tungsten arc welding-based wire arc additive manufacturing under controlled oxygen concentrations of 1, 500 and 1000 ppm, with ambient air used as a severe oxygen-exposure reference. The effects of oxygen concentration on oxygen uptake, microstructure, oxidation behavior and mechanical properties were investigated. Within the controlled range, the internal oxygen content increased from 0.07 to 0.15 wt.%, remaining below the ASTM B381-2013 limit. These specimens retained sound interlayer bonding and were mainly composed of α-Ti with a small amount of β-Ti, without detectable crystalline TiO₂ by X-ray diffraction. Controlled oxygen uptake refined the α lamellae and increased deformation resistance through interstitial solid-solution strengthening, increasing hardness from approximately 320 HV to 330–350 HV and tensile strength from 880 to 940 MPa, while reducing elongation from 11.5% to 9.5%. In contrast, the ambient-air specimen reached an oxygen content of 0.36 wt.%, developed an approximately 90 μm oxidation-affected layer and showed TiO₂-related oxides, α-colony aggregation and interface weakening. Its tensile strength and elongation decreased sharply to 295 MPa and 1.9%, respectively. These results indicate that atmosphere control in WAAM Ti-6Al-4V should prevent the transition from controlled oxygen strengthening to excessive oxygen-induced embrittlement. Full article

Wall thickness affects local heat accumulation during laser powder bed fusion (LPBF), but its role in governing the as-built martensitic morphology and tensile response of near-α TA15 alloy remains unclear. In this study, TA15 walls with thicknesses from 0.5 mm to 30 mm were fabricated under identical LPBF parameters. Optical microscopy, scanning electron microscopy, electron backscatter diffraction, tensile testing, fractography, and finite-element thermal simulation were used to correlate wall-thickness-dependent cyclic reheating with α′ lath evolution and mechanical behavior. Increasing wall thickness promoted α′ lath coarsening and the formation of colony-like lath structures with enlarged similarly oriented regions. The average α′ lath width increased from approximately 0.28 μm in the 0.5-T specimen to 1.55 μm in the 30-T specimen. The yield strength reached a maximum of 972.3 ± 5.29 MPa in the 1-T specimen, whereas elongation increased from 9.5 ± 0.6% to 17.8 ± 1.7% with increasing wall thickness. These results indicate a strong correlation between wall-thickness-dependent cyclic reheating, α′/α lath coarsening, lath-network evolution, and tensile-property variation in LPBF-fabricated TA15 alloy. Full article

Accurate prediction of mechanical properties is essential for quality control in hot strip rolling (HSR), where the relationships among chemical composition, process parameters, and mechanical properties are highly nonlinear under industrial conditions. In this work, a data-driven framework was established for the prediction and interpretation of yield strength (YS), tensile strength (TS), and elongation (EL) of hot-rolled strips based on industrial production data. A high-quality dataset was constructed through data collection, outlier removal, and feature selection. Six machine learning (ML) models were developed and compared, and particle swarm optimization (PSO) was employed for hyperparameter tuning. The results showed that random forest (RF) achieved the best overall predictive performance, with R² values of 0.979, 0.986, and 0.959 for YS, TS, and EL, respectively. In addition, faster convergence and better optimization performance were obtained by PSO than by genetic algorithm (GA) and artificial bee colony (ABC). Shapley additive explanations (SHAP) were further introduced to reveal both global feature importance and local feature contributions. The proposed framework provides an effective approach for mechanical property prediction and alloy design in HSR. Full article

Due to their characteristics of a high power-to-weight ratio, stringent lightweight requirements, and harsh working environments, straight face gears are prone to issues such as tooth fracture and inadequate fatigue strength. Meanwhile, because of the lack of fatigue information and weak fatigue life prediction method, the fatigue life of face gears cannot be effectively evaluated. In this study, the key technologies involved in the hot rolling forming process, fatigue experiments, and numerical modeling of straight face gears are studied. A technical foundation for straight face gears formed by hot rolling processing is established, and a fatigue experiment of the hot rolling forming of straight face gears is carried out. Due to the lack of information on fatigue experiments, a numerical prediction model is constructed. Sample expansion is carried out using a BP neural network–Bootstrap model to calculate the reliable lifespan of hot-rolled straight face gears, and fatigue life prediction for hot-rolled straight face gears is completed via the improved GM(1,1,λ) model based on the artificial bee colony algorithm, and thus the accurate evaluation of the fatigue life of rolling forming face gears is realized. The feasibility and superiority of the improved fatigue life prediction model are demonstrated by comparing it with the traditional prediction model and experimental results. The theoretical basis and technical support for the research of the fatigue resistance and installation application of face gears are provided. Full article

Pulsed electromagnetic fields (PEMFs) may exert antimicrobial effects, which could be relevant both in medical applications and as a contributing factor in electro-disinfection processes. This study was designed to evaluate their impact on the viability of Pseudomonas aeruginosa (ATCC 27853). Experiments were performed in three independent biological replicates, each with three technical replicates per group. Groups 1–3 served as controls and were not exposed to PEMFs. Groups 4–6, 7–9, and 10–12 were exposed to PEMFs of 40, 60, and 80 µT, respectively, for 4, 8, and 24 h using a cylindrical copper solenoid coil. Bacterial viability was assessed via colony-forming unit (CFU) counts, and log₁₀ CFU/mL values were reported. Transmission electron microscopy (TEM) was used to examine structural changes in bacterial cells. PEMF exposure significantly reduced P. aeruginosa viability, with magnetic field strength (p < 0.001), exposure time (p < 0.01), and their interaction (p < 0.05) showing significant effects. Post hoc analysis revealed that higher field strengths, particularly 80 µT after 24 h, produced the greatest reduction in CFU counts, whereas 40 µT showed no significant difference compared to controls (p > 0.05). TEM images demonstrated pronounced degeneration and structural damage in PEMF-exposed bacterial cells. PEMF exposure reduced CFU counts in an intensity and duration-dependent manner. While a dose-related trend is suggested, limited experimental conditions preclude definitive conclusions, and findings should be interpreted cautiously due to the in vitro design. Full article

Yarn tenacity is one of the vital quality parameters that determine the performance, fabric durability and end use suitability. The tenacity of yarn is largely influenced by the fibre characteristics used. The physical properties of jute fibres, including root content, defect, bundle strength, and fineness, exert a significant influence on yarn tenacity. This study utilized metaheuristic optimized random forest regression (RFR) to predict jute yarn tenacity from fibre parameters. The hyperparameters of the RFR models were optimized using four metaheuristic algorithms: whale optimization algorithm (WOA), grey wolf optimization (GWO), beetle antennae search (BAS) and ant colony optimization (ACO). The model utilized a dataset comprising 414 experimental data with 70% data for training and 30% for testing the model, using input variables such as bundle strength (g/tex), defects (%), root content (%) and fineness (tex) to predict yarn tenacity (cN/tex). The developed models effectively predicted yarn tenacity. However, RFR–GWO achieved slightly better performance with R² of 1.0 for training set and 0.96 for test set. Regarding execution time, RFR–GWO is the fastest requiring only 14.25 s. SHAP analysis revealed that bundle strength and root content of jute fibre are the most influential factors, whereas defect and fineness exert the least influence on model’s prediction. The best model RFR–GWO was deployed into an interactive Streamlit web application, offering an intuitive and user-friendly platform for the real-time estimation of yarn tenacity. Full article

Recent evidence implicates the gut microbiota in muscle physiology and function via the gut–muscle axis, which portrays bidirectional communication between microbial colonies, their metabolites and muscle tissue. Age-related muscle decline, including sarcopenia and muscle atrophy, has been associated with shifts in gut microbiota composition and lower levels of microbial metabolites, such as short-chain fatty acids (SCFAs), thereby expanding muscle health research toward microbiota-based therapies. Postbiotics, defined as preparations of inanimate microorganisms and/or their components, are gaining attention as a novel approach to combating muscle decline through modulation of microbiota–host communication, yet a comprehensive review of this topic is currently lacking. Preclinical studies demonstrate that postbiotics may exert anabolic effects while attenuating catabolism, inflammation, and cellular senescence, with associated improvements in grip strength, endurance capacity, and muscle morphology. Although clinical evidence remains limited, available studies indicate that postbiotics may have beneficial effects on muscle strength, endurance, and overall physical performance in humans. By synthesizing recent preclinical and clinical evidence, this review addresses an important gap in the literature, offering a comprehensive and mechanistically informed perspective on the potential role of postbiotics in modulating muscle health, particularly in the context of sarcopenia- and atrophy-associated muscle phenotypes. Full article

Despite their potential to improve properties such as the high-temperature strength required for jet engine blades, low-Al TiAl alloys have largely been overlooked. The most significant challenge is ensuring impact resistance, which is crucial for jet engine blade applications. First, this study evaluated the impact resistance of fully lamellar Ti-38.75–50.25 Al binary alloys in relation to the effects of α₂ phase ratio and spacing using a Charpy impact test. Subsequently, the impact of reducing Al content in Cr-added forged alloys and cast TiAl4822 was investigated. The results revealed that α₂ phase spacing had the most significant impact on impact resistance at 800 °C. Coarse α₂ phase spacing of approximately 6 μm, created in the high-Al material, provided the highest impact resistance. In contrast, the impact resistance of the low-Al material was low due to its extremely narrow α₂ phase spacing. In forged alloys, reducing both Al content and β-stabilizing elements enabled the removal of the deleterious β phase through heat treatment, while maintaining good forgeability, thereby improving impact resistance and creep strength. In low-Al TiAl4822, the expected improvement in creep strength could not be achieved because the low-strength γ phase located at lamellar colony boundaries underwent preferential deformation. Full article

Poor nutrition is a known contributing factor to ongoing high rates of honey bee colony mortality. Beekeepers invest significant resources to provide supplemental feeds to their colonies, but currently available diets are nutritionally incomplete. To test whether commercially managed colonies fed a manufactured, nutritionally complete Pollen-Replacing Feed (PRF-1) would exhibit improved colony health outcomes compared to beekeeper-selected Commercial Standard Feeds, we tracked colony health metrics from fall through almond pollination and the subsequent spring in a large-scale, multi-year field trial. By January (in almonds), PRF-1-fed colonies had 1.19 more frames of bees (p < 0.001) and 18.7% more colonies meeting the 8-frame minimum size requirement for high-revenue pollination contracts. After almond pollination (March), PRF-1-fed colonies exhibited a 13.8% increase in survival (p = 0.002), 2.57 more frames of bees (p = 0.006), and 0.79 more frames of brood (p = 0.003). PRF-1-fed colonies also exhibited superior spring build-up, adding 1.22 more frames of bees between January and March (p = 0.03). Economically, a hypothetical 100-colony operation fed PRF-1 garnered an additional $12,065.81 in gross revenue in the first year. Nutritional benefits are projected to compound, leading to exponentially increased revenue over subsequent years. Persistent improvements in colony health demonstrate that providing a nutritionally complete pollen-replacing feed in fall and winter has a long-lasting, positive impact on colony health and commercial viability. Full article

The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, rely on fixed-step gradient-based control, which leads to steady-state oscillations around the maximum power point, slow convergence during rapid irradiance and temperature variations, and inaccurate tracking under partial shading conditions. These technical limitations often cause the controller to deviate from the global maximum power point, resulting in reduced dynamic efficiency, increased power losses, and degraded power quality in practical PV applications. To overcome these limitations, this research proposes a hybrid optimization model that incorporates ACO (Ant Colony Optimization) and an ANN (Artificial Neural Network) to enrich the effectiveness of MPPT in PV systems. The proposed model is designed to dynamically adapt to variations in solar irradiance and temperature, effectively addressing the inadequacies present in the conventional techniques and also improving the MPPT efficiency in PV systems. By leveraging the unique strengths of both ACO and ANN, the model significantly improves energy extraction and also ensures robustness against environmental fluctuations. Simulation results demonstrate that the proposed ACO–ANN MPPT framework achieves a total harmonic distortion (THD) of 1.39%, representing a reduction of approximately 34–70% compared to conventional and recent AI-based MPPT techniques, while simultaneously delivering higher voltage stability, faster convergence, and increased maximum power extraction. This contribution is vital in paving the way for future advancements in renewable energy systems and provides a more reliable approach to solar power optimization, which can greatly aid in achieving sustainable energy goals. Full article

Honey bee pollination of entomophilous commercial crops is a major input in agricultural management yet unlike irrigation, fertilisation and plant protection have yet to be integrated into precision agriculture practices. This study examines colony strength as a key determinant of efficient pollination. Over three years and across two study sites, we evaluated the relationship between colony strength (frames of bees, FOBs) and colony productivity using continuous hive weight monitoring. Hive weight data were analysed for both absolute gains and relative gains normalised per FOB across colony strengths. In all study periods, stronger colonies showed disproportionately higher weight gains compared to weaker colonies. For each additional FOB, the average increase in normalised weight gain ranged from 0.1 to 0.41 kg per colony, indicating a non-linear relationship between colony strength and productivity. An efficiency factor calculated for groups of strong and weak colonies ranged from 1.2 to 2.6, depending on the season and crop. Moreover, during periods of forage dearth, strong colonies exhibited lower weight losses than the weak colonies per FOB, making them more efficient under resource limited conditions. Our findings demonstrate that colony strength significantly influences foraging efficiency and colony resilience, ultimately supporting the conclusion that fewer stronger colonies will improve pollination outcomes while reducing the economic and environmental costs associated with commercial pollination services. Full article

The presence of the brittle β/B2 phase in TiAl alloys often deteriorates their mechanical properties, posing a significant challenge for manufacturing large-sized, high-performance sheets. To address this issue, this study systematically investigates the synergistic effect of pack rolling and subsequent heat treatment on the microstructure evolution and mechanical properties of a Ti-44Al-4Nb-1.5Mo-0.1B-0.1Y alloy. Sheets with two different deformation levels (R7: 69.8% and R11: 83.0% reduction) were prepared via pack rolling. This was followed by a series of heat treatments at different temperatures (1150–1350 °C) and cyclic heat treatments at 1250 °C (3, 6, and 9 cycles). The results demonstrate that the higher deformation level (R11) promoted extensive dynamic recrystallization, resulting in a uniform microstructure of equiaxed γ, α₂, and β phases, while the lower deformation (R7) retained a significant fraction of deformed γ/α₂ lamellae. Heat treatment at 1250 °C was identified as optimal for transforming the microstructure into fine lamellar colonies while effectively reducing the β/B2 phase. Cyclic heat treatment at this temperature further decreased the β-phase content to 4.1% after 9 cycles. The elimination mechanism was determined to follow the β→ α → γ + α₂ phase transformation sequence, driven by the combined effect of rolling-induced defects and cyclic thermal stress. Cyclic heat treatment at this temperature was particularly effective in generating a high density of nucleation sites within the lamellar colonies, leading to significant refinement of the lamellar structure. Consequently, the R11 sheet subjected to 9 cycles of heat treatment exhibited a 15.5% increase in tensile strength and an 8.3% improvement in elongation compared to the hot-isostatically pressed state. This enhancement is primarily attributed to the significant refinement of lamellar colonies and the reduction in interlamellar spacing. This work presents an effective integrated processing strategy for fabricating high-performance TiAl alloy sheets with superior strength and toughness. 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