Search Results (621)

In this study, steel fibers were used to improve the mechanical properties of high-strength self-compacting concrete (HSSCC), and its effect on the fracture mechanical properties was investigated by a three-point bending test with notched beams. Coupled with the digital image correlation (DIC) technique, the fracture process of steel-fiber-reinforced HSSCC was analyzed to elucidate the reinforcing and fracture-resisting mechanisms of steel fibers. The results indicate that the compressive strength and flexural strength of HSSCC cured for 28 days exhibited an initial decrease and then an enhancement as the volume fraction (V_f) of steel fibers increased, whereas the flexural-to-compressive ratio linearly increased. All of them reached their maximum of 110.5 MPa, 11.8 MPa, and 1/9 at 1.2 vol% steel fibers, respectively. Steel fibers significantly improved the peak load (F_P), peak opening displacement (CMOD_P), fracture toughness (K_IC), and fracture energy (G_F) of HSSCC. Compared with HSSCC without steel fibers (HSSCC-0), the F_P, K_IC, CMOD_P, and G_F of HSSCC with 1.2 vol% (HSSCC-1.2) increased by 23.5%, 45.4%, 11.1 times, and 20.1 times, respectively. The horizontal displacement and horizontal strain of steel-fiber-reinforced HSSCC both increased significantly with an increasing V_f. HSSCC-0 experienced unstable fracture without the occurrence of a fracture process zone during the whole fracture damage, whereas the fracture process zone formed at the notched beam tip of HSSCC-1.2 at its initial loading stage and further extended upward in the beams of high-strength self-compacting concrete with a 0.6% volume fraction of steel fibers and HSSCC-1.2 as the load approaches and reaches the peak. Full article

There is insufficient evidence regarding the cytotoxicity of restorative 3D-printing resins, used as part of the digital workflow in dentistry. This study presents a novel comparative evaluation of cell viability and adhesion using human Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs), a less commonly used but clinically relevant cell line in dental biomaterials research. The aim of this study was to evaluate the cell viability of WJ-MSCs seeded on 3D-printed resins intended for temporary restorations. Resin discs of three commercial 3D-printing resins (NextDent C&B, Leaf Dental C&B, and UNIZ Temp) and a conventional self-curing acrylic resin (NicTone) were used. WJ-MSCs were cultured on the specimens for 1, 4, and 10 days. Cell viability was assessed using the PrestoBlue assay, Live/Dead immunofluorescence staining, and 7AAD/Annexin V staining. Cell adhesion was evaluated using scanning electron microscopy. Direct exposure to the 3D-printed resins and the self-curing acrylic caused slight reductions in cell viability compared to the control group in both microscopic analyses. 7AAD/Annexin V showed the highest percentage of viable WBCs for the conventional acrylic (34%), followed by UNIZ (35%), NextDent (42%), and Leaf Dental (36%) (ANOVA p < 0.05 Tukey’s post-hoc test p < 0.05). These findings suggest that 3D-printed resins could be considered safe for use in temporary restorations. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

Currently, numerous conventional airport runways suffer from cracking distresses and cannot meet their structural and functional requirements. To address the urgent demand for rapid and durable maintenance of airport runways, this study investigates the material optimization and curing behavior of cold-mix epoxy asphalt (CEA) for non-disruptive overlays. Eight commercial CEAs were examined through tensile and overlay tests to evaluate their strength, toughness, and reflective cracking resistance. Two high-performing formulations (CEA 1 and CEA 8) were selected for further curing characterization using differential scanning calorimetry (DSC) tests, and the non-isothermal curing kinetics were analyzed with different contents of Component C. The results reveal that CEA 1 and CEA 8 were selected as promising formulations with superior toughness and reflective cracking resistance across a wide temperature range. DSC-based curing kinetic analysis shows that the curing reactions follow an autocatalytic mechanism, and activation energy decreases with conversion, confirming a self-accelerating process of CEA. The addition of Component C effectively modified the curing behavior, and CEA 8 with 30% Component C reduced curing time by 60%, enabling traffic reopening within half a day. The curing times were accurately predicted for each type of CEA using curing kinetic models based on autocatalytic and iso-conversional approaches. These findings will provide theoretical and practical guidance for high-performance airport runway overlays, supporting rapid repair, extended service life, and environmental sustainability. Full article

This study investigates the influence of varying water-to-cement (W/C) ratios and fine aggregate compositions on the performance of concrete incorporating expanded perlite aggregate (EPA) as a lightweight alternative to natural sand. A total of eighteen concrete mixes were produced, each with different W/C ratios and fine-to-coarse aggregate (FA/CA) ratios, and evaluated for workability, compressive strength, flexural and tensile strength, water absorption, density, and thermal conductivity. Perlite was used to fully replace natural sand in half of the mixes, allowing a direct assessment of its effects across low-, medium-, and high-strength concrete formulations. The results demonstrate that EPA can improve workability and reduce both density and thermal conductivity, with variable impacts on mechanical performance depending on the W/C and FA/CA ratios. Notably, higher cement contents enhanced the internal curing effect of perlite, while lower-strength mixes experienced a reduction in compressive strength when perlite was used. These findings suggest that expanded perlite can be effectively applied in structural and non-structural concrete with optimized mix designs, supporting the development of lightweight, thermally efficient concretes. Mixture W16-100%EPS was considered the ideal mix because its compressive strength at the age of 65 days 44.2 MPa and the reduction in compressive strength compared to the reference mix 14% and the reduction in density 5.4% compared with the reference mix and the reduction in thermal conductivity 14% compared with the reference mix. Full article

Two-component dental materials are commonly used by the dentist for various applications (cementation of indirect restorations, filling of a cavity without layering, etc.). These materials are cured by redox polymerization. The (hydro)peroxide/thiourea/copper salt redox initiator system is well established and can be found in a wide range of commercially available dental materials. The thiourea is a key component of the initiator system. This study explores the influence of the nature of the thiourea reducing agent on the reactivity and efficiency of redox initiator systems. In this work, six different thiourea structures were investigated, in combination with copper(II) acetylacetonate and cumene hydroperoxide (CHP), to understand their impact on polymerization kinetics and mechanical properties of methacrylate-based materials. Various experimental techniques, including mass spectrometry (MS) and spectroscopic analyses, were employed to elucidate the underlying mechanisms governing these redox systems. The results highlight that thiourea plays a dual role, acting both as a reducing agent and as a ligand in copper complexes, affecting radical generation and polymerization efficiency. Structural modifications of thiourea significantly influence the initiation process, demonstrating that reactivity is governed by a combination of factors rather than a single property. Self-cure dental flowable composites exhibiting excellent flexural strength (>100 MPa) and modulus (>6000 MPa) were obtained using hexanoyl thiourea, N-benzoylthiourea, or 1-(pyridin-2-yl)thiourea as a reducing agent. The adjustment of the Cu(acac)₂ enables to properly set the working time in the range of 100 to 200 s. These findings provide valuable insights into the design of the next generation of redox initiating systems for mild and safe polymerization conditions. Full article

To address the high drying temperature, low yield, and low coating rate that characterize traditional chitosan/gum arabic microcapsules, this study used chitosan/sodium tripolyphosphate (STPP) ionic crosslinking to construct a composite wall, combined with optimized emulsifier compounding (T-80/SDBS), to prepare tung oil self-healing microcapsules. Orthogonal testing determined the following optimal parameters: a core-to-wall ratio of 2.0:1.0, a T-80/SDBS ratio of 4.0:6.0 (HLB = 12.383), an STPP concentration of 4%, and a spray-drying temperature of 120 °C. With these parameters, a yield of 42.91% and coating rate of 68.50% were achieved. The microcapsules were spherical (1–6 μm), with chitosan–STPP electrostatic interactions forming a dense wall. Adding 5% microcapsules to the UV topcoat enabled self-healing after 60 s UV curing: the scratch-healing rate reached 25.25% (width decreased from 11.13 μm to 8.32 μm), the elongation at break increased by 110% to 9.31%, the light transmission remained >82.50%, and the color difference (ΔE = 2.16) showed no significant change versus unmodified coating. Full article

The repair efficiency of various self-healing materials often depends on the ability of the prepolymer and curing agent to form mixtures. This paper presents a synthesis and study of the properties of modified self-healing polyurethanes using the Diels–Alder reaction (DA reaction), obtained from a maleimide-terminated preform and a series of furan–urethane curing agents. The most commonly used isocyanates (4,4′-methylene diphenyl diisocyanate (MDI), 2,4-tolylene diisocyanate (TDI), and hexamethylene diisocyanate (HDI)) and furan derivatives (furfurylamine, difurfurylamine, and furfuryl alcohol) were used as initial reagents for the synthesis of curing agents. For comparative analysis, polyurethanes were also obtained using the well-known “traditional” approach—from furan-terminated prepolymers based on mono- and difurfurylamine, as well as furfuryl alcohol and the often-used bismaleimide curing agent 1,10-(methylenedi-1,4-phenylene)bismaleimide (BMI). The structure and composition of all polymers were studied using spectroscopic methods. Molecular mass was determined using gel permeation chromatography (GPC). Thermal properties were studied using TGA, DSC, and TMA methods. The mechanical and self-healing properties of the materials were investigated via a uniaxial tensile test. Visual assessment of the completeness of damage restoration after the self-healing cycle was carried out using a scanning electron microscope. It was shown that the proposed modified approach helps obtain more durable polyurethanes with a high degree of self-healing of mechanical properties after damage. Full article

Background/Objectives: This review aimed to compare the efficacy of antibiotic treatment vs. non-antibiotic treatment in mild and moderate clinical mastitis in lactating dairy cows, categorized by the causative pathogen. Methods: The initial systematic review plan, which resulted in only four relevant articles, was altered due to limited available studies and significant heterogeneity among them. Consequently, five additional articles, closely meeting our criteria with minor differences, were included to ensure comprehensive analysis, resulting in nine included articles. Due to these pragmatic constraints, this review represents a hybrid between a systematic and a narrative review. The outcome of interest was the bacteriological cure (BC). Results: The findings revealed that antibiotic treatment resulted in improved BC rates for cases caused by Streptococci. For cases caused by Escherichia (E.) coli, antibiotic therapy showed no significant improvement in BC rates compared to non-antibiotic treatment, suggesting that antibiotics may be often unnecessary for these cases due to self-limiting tendencies. However, severe E. coli mastitis warrants systemic antibiotic treatment due to potentially life-threatening complications. Klebsiella spp. mastitis showed better cure rates with antibiotic therapy. Conclusions: This study underscores the importance of regular pathogen diagnostics to guide appropriate treatment, advocating for the use of on-farm rapid tests to reduce unnecessary antibiotic use while ensuring effective treatment outcomes. Full article

Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10⁻⁴, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads. Full article

Composite solid propellants, which serve as the core energetic materials in aerospace and military propulsion systems, necessitate tailored enhancement of their mechanical properties to ensure operational safety and stability. A critical challenge involves elucidating the interfacial interactions among the multiple propellant components (≥6 components, including the polymer binder HTPB, curing agent IPDI, oxidizer particles AP/Al, bonding agents MAPO/T313, plasticizer DOS, etc.) and their influence on crosslinked network formation. In this study, we propose an integrated computational framework that combines coarse-grained simulations with reactive force fields to investigate these complex interactions at the molecular level. Our approach successfully elucidates the two-step reaction mechanism propagating along the AP interface in multicomponent propellants, comprising interfacial self-polymerization of bonding agents followed by the participation of curing agents in crosslinked network formation. Furthermore, we assess the mechanical performance through tensile simulations, systematically investigating both defect formation near the interface and the influence of key parameters, including the self-polymerization time, HTPB chain length, and IPDI content. Our results indicate that the rational selection of parameters enables the optimization of mechanical properties (e.g., ~20% synchronous improvement in tensile modulus and strength, achieved by selecting a side-chain ratio of 20%, a DOS molar ratio of 2.5%, a MAPO:T313 ratio of 1:2, a self-polymerization MAPO time of 260 ns, etc.). Overall, this study provides molecular-level insights into the structure–property relationships of composite propellants and offers a valuable computational framework for guided formulation optimization in propellant manufacturing. Full article

Background/Objectives: A material incompatibility has been established between self-etching adhesives and amine-containing dual-cure resin composite materials used for core buildups. This study aims to compare the dentin bond strength of several amine-containing and amine-free core materials using self-etching adhesives with different pHs. Methods: Extracted human molars were mounted in acrylic and ground flat with 320-grit silicon carbide paper. Next, 520 specimens (n = 10/group) were assigned to a dual-cure core buildup material group (10 amine-containing, 2 amine-free, and 1 reference light-cure only bulk fill flowable composite) and assigned to a self-etching adhesive subgroup (pH levels of approximately 1.0, 3.0, and 4.0). Within 4 h of surface preparation, the adhesive corresponding to the specimen’s subgroup was applied and light-cured. Composite buttons for the assigned dual-cure core material of each group were placed using a bonding clamp apparatus, allowed to self-cure for 2 h at 37 °C, and then unclamped. An additional group with one adhesive (pH = 3.0) was prepared in which the dual-cure core materials were light-cured. The bonded specimens were stored in water at 37 °C for 24 h. The specimens were mounted on a testing clamp and de-bonded in a universal testing machine with a load applied to a circular notched-edge blade at a crosshead speed of 1 mm/min until bond failure. The maximum load divided by the area of the button was recorded as the shear bond strength. The data was analyzed via 2-way ANOVA. Results: The analysis of bond strength via 2-way ANOVA determined statistically significant differences between the adhesives, the core materials, and their interaction (p < 0.01). There was a general trend in shear bond strength for the adhesives, where pH 4.0 > 3.0 > 1.0. The amine-free core materials consistently demonstrated higher shear bond strengths as compared to the other core materials when chemically cured only. Light-curing improved bond strength for some materials with perceived incompatibility. Conclusions: The results of this study suggest that an incompatibility can exist between self-etching adhesives and dual-cure resin composite core materials. A decrease in the pH of the utilized adhesive corresponded to a decrease in the bond strength of dual-cure core materials when self-curing. This incompatibility may be minimized with the use of core materials formulated with amine-free chemistry. Alternatively, the dual-cure core materials may be light-cured. Full article

Background and Objectives: Ensuring the optimal shear bond strength (SBS) is essential for the long-term success of prosthodontic restorations. Our in vitro study aimed to evaluate the SBS of three types of ceramics (feldspathic, alumina, and lithium disilicates) using three adhesive cements (Variolink Esthetic LC, Variolink Esthetic DC, and Maxcem Elite). Materials and Methods: Healthy molars were prepared, and ceramic blocks were cemented following universally accepted luting protocols. SBS tests were performed using a custom-made testing machine. A multiple linear regression model assessed the effects of ceramic type, surface treatment, and luting agent on SBS. Results: The regression model explained 61.3% of the variation in SBS values (R² = 0.613); the adjusted R² = 0.605 confirmed the model’s robustness. The global F-test was statistically significant (F = 78.96, p < 0.001). The total-etch technique (+4.47), the use of feldspathic ceramic, and 5% hydrofluoric acid treatment (+3.28) significantly affected SBS. Feldspathic ceramic and lithium disilicate showed superior performance against alumina. Light-cured and self-cured cement showed negative effects. Conclusions: Ceramic material and cement type have combined effects on SBS. Optimal results were obtained with the total-etch technique, feldspathic ceramic, 5% hydrofluoric acid treatment, and dual-cured cement. Full article

This study investigates a prediction method for the compressive strength of concrete considering the pore relative humidity. Water within concrete not only facilitates the bonding of cementitious materials and aggregates but also influences the pore structure, thus affecting the compressive strength of concrete. While the relationship between the water–cement ratio and mechanical properties has been extensively explored, the quantitative effects of curing and moisture history on compressive strength remain insufficiently demonstrated. This research aims to fill this gap by proposing predictive models that consider the history of pore humidity. Experimental data from previous studies were utilized to develop and verify these models. Pore humidity was assessed through self-desiccation and diffusion processes. A self-desiccation model was formulated based on existing experimental results, and the finite element method was employed for diffusion analysis. The prediction model for compressive strength was derived from the rate constant model, incorporating apparent activation energy and adjusting for various curing conditions. The proposed models provide a robust framework for predicting the compressive strength of concrete under diverse curing scenarios. This research contributes to the development of practical tools for ensuring the safety and durability of concrete structures in the construction industry. Full article

This study advances the development of self-sensing concrete through functionalization with carbon nanotubes (CNTs) for structural health monitoring. Through experimental analyses, it relies on its dual responsiveness to mechanical and thermal stimuli. Three-point bending and thermal tests were systematically conducted on concrete samples with CNT concentrations ranging from 0 to 0.05 wt.% of cement, evaluated at 7- and 28-day curing periods. Mechanical testing demonstrated curing-dependent behavior: At 7 days, mechanical strength and electrical current response exhibited pronounced variability across CNTs loadings, with optimal balance achieved at 0.01% CNTs. At 28 days, the tests show that the mechanical properties are relatively stabilized, reaching the highest value at 0.006 wt.% CNTs and achieving the best electrical sensitivity at 0.01 wt.% CNTs. The thermal experiments revealed faster current modulation in the 7-day samples than in the 28-day counterparts, with intermediate CNT concentrations (e.g., 0.01 wt.%) showing a more sensitive response. The sensitivity was analyzed for both mechanical and thermal changes to further evaluate the feasibility of using CNT-reinforced concrete as a sensor material. Conductivity measurements on fully cured samples indicated that all samples exhibited electrical conductivities in the 10⁻⁴ S/m range, suggesting semiconductive behavior, while 0.006 wt.% CNTs yielded the highest conductivity. Higher CNT content did not further improve conductivity, likely due to agglomeration disrupting the network. These findings confirm CNT-modified concrete’s dual electromechanical and thermal responsiveness and support its potential as a multifunctional sensing material. Full article