Search Results (3,962)

Hot-filament chemical vapor deposition (HFCVD) facilitates the realization of industrial mass production owing to its simple synthesis device, facile control of process conditions, and low preparation cost. Reactive pressure is one of the deposition parameters that exert a profound influence on the growth of HFCVD diamond films on polycrystalline diamond (PCD) substrates, primarily affecting the growth rate and grain size of the deposited diamond coating. A univariate experimental approach was employed to investigate the effects of reactive pressure (2 kPa, 3 kPa, 4 kPa, 5 kPa) on the properties of as-deposited diamond films. The results show that with the increase in reactive pressure, the growth rate increased first and then decreased, peaking at 5.366 μm/h at 3 kPa. The fractal dimension and grain size follow a similar variation trend, both decreasing first and then increasing. The grain size drops to 15.8 nm when the reactive pressure is 3 kPa, at which point the adhesive strength of the film is maximized. This phenomenon can be attributed to the fact that excessively low reactive pressure extends the mean free path of particles and active species, endowing them with higher kinetic energy and reducing collision-induced energy loss. This in turn significantly promotes diamond nucleation, secondary nucleation and grain refinement, thus facilitating the growth of nanocrystalline diamond. In contrast, an excessively high pressure yields the opposite effect, inhibiting nucleation and promoting grain coarsening. Full article

Background/Objectives: The goal of this study was to characterize changes in autofluorescence of epithelial cells obtained from saliva stains that occur with time and investigate the potential for these changes to serve as time-since-deposition (TSD) signatures for this sample type. Methods: Saliva from 50 individuals was used to create 208 deposits that were aged between one day and nine months. Autofluorescence profiles of individual cells were obtained from each sample using imaging flow cytometry (IFC) and analyzed across nine different emission channels ranging between 435 nm and 800 nm. Results: Results showed strong evidence for linear increases in autofluorescence intensity when epithelial cells from a single donor deposit were measured over time (12 of 14 donors r ≥ 0.9). When autofluorescence profiles from all 50 donors were combined into a single time series, variation in autofluorescence intensity was observed between individual deposits with the same TSD. This inter-contributor variation decreased the overall strength of the linear relationship (r = 0.83) and yielded residual errors of ~8 days for samples that were actually 1 day old and ~82 days for samples that were over 180 days old using a linear regression model. Although this approach may not currently be amenable to estimating TSD to the day with high accuracy, clear, non-overlapping differences in autofluorescence intensity were still observed between certain time intervals, e.g., saliva deposits that were aged for 1 day compared to saliva deposits that were aged for more than 120 days. Conclusions: This suggests that cellular autofluorescence signatures have the potential to be probative when hypotheses for sample deposition involve disparate time intervals or as a screening tool for identifying which samples are most likely relevant to the crime in question based on their deposition time. Full article

Steel–UHPC composite beams are widely used in bridge engineering due to their high strength, durability, and suitability for prefabricated construction. However, the mechanical performance of shear connectors in UHPC differs significantly, and the uniform use of a single connector type along the beam span may result in a mismatch between connector mechanical characteristics and regional force demands, leading to suboptimal force transfer and inefficient utilization of connector capacity along the beam span. While previous studies have mainly focused on the local behavior of individual connectors, a system-level design strategy considering regional force demands is still limited. This study proposes a system-level design method for combined shear connectors in steel–UHPC composite beams, in which headed stud connectors and trapezoidal composite dowel connectors are arranged according to bending moment distribution and interface shear demand, thereby integrating connector mechanical characteristics with the spatial variation in internal forces along the beam span. The design procedure includes shear span division, longitudinal interface shear calculation, and resistance verification of different connector types. The method is applied to a practical steel–UHPC composite beam in a long-span approach bridge. Results show that headed studs provide reliable uplift resistance and ductile behavior in negative bending regions, whereas composite dowel connectors are shown to be more suitable for shear-dominated positive bending regions due to their higher shear capacity and stiffness. The combined system ensures effective composite action under different stress states and reduces total connector steel consumption compared with a stud-only layout. The proposed approach advances connector design toward performance-oriented and system-level structural optimization, providing a practical framework for connector arrangement in steel–UHPC composite beams. Full article

Internet Gaming Disorder (IGD) has become a major behavioral health concern among adolescents, yet current assessment tools remain limited. These tools often fail to capture the disorder’s complex symptom variations and lack clinical interpretability. This study, taking an interdisciplinary approach that combines clinical psychology and psychometrics, summarizes recent progress in understanding adolescent IGD and the development of its assessment methods. We compare the diagnostic criteria of the DSM-5 TR and ICD-11 and argue that the nine DSM-5 TR criteria are particularly suited for transformation into distinct diagnostic attributes due to their detailed and actionable nature. We then review the strengths and weaknesses of Classical Test Theory (CTT), Item Response Theory (IRT), and Cognitive Diagnostic Models (CDMs) in assessing IGD. The review emphasizes the limitations of total-score and single latent-trait approaches in capturing the disorder’s multidimensional symptoms. Based on these insights, we propose a conceptual assessment framework, Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT), that integrates CDMs with computerized adaptive testing. Rather than presenting an empirically validated system, this framework offers a theoretically grounded proposal that specifies the key components, logical relationships, and methodological pathways necessary for advancing precision assessment of adolescent IGD. CD-CAT uses a system of attributes and a Q-matrix based on the DSM-5 TR criteria to efficiently classify IGD symptoms in adolescents, reducing the number of items required while enhancing clinical relevance. Lastly, we discuss the theoretical contributions of the proposed framework, acknowledge its limitations as a conceptual proposal, and outline directions for future empirical research. Full article

Autoencoder-based models have become a fundamental component of unsupervised and self-supervised learning in natural language processing (NLP), enabling models to learn compact latent representations through input reconstruction. From early denoising autoencoders to probabilistic variational autoencoders (VAEs) and transformer-based masked autoencoding, reconstruction-driven objectives have played a significant role in shaping modern approaches to text representation and generation. This review provides a comprehensive analysis of the evolution of autoencoder architectures and training objectives in NLP, and synthesizes applications of VAEs across language modeling, controllable text generation, machine translation, sentiment modeling, and multilingual representation learning. Although previous surveys have examined deep generative models or representation learning in NLP, there remains a lack of a unified review that systematically connects classical autoencoder variants, variational formulations, and modern transformer-based masked autoencoders within a single conceptual framework. To address this gap, this work consolidates architectural developments, training objectives, and major application domains under a reconstruction-based learning perspective, offering a structured comparison of modeling choices, datasets, and evaluation practices. Our analysis highlights the strengths and limitations of existing approaches, discusses the ongoing influence of autoencoder-style learning in NLP, and outlines future research directions focused on improving training stability, designing more structured latent spaces, and enhancing multilingual representation learning. Full article

Objective: To determine the most suitable core build-up materials based on their mechanical and physical properties, different resin based materials were evaluated for flexural strength (FS), flexural modulus (E), modulus of resilience (R), water sorption (WS), and solubility (SO). Materials and Methods: Three dual-cure resins (CosmeCore DC Automix, CCC; Clearfil DC Core Plus, CCP; MultiCore Flow, CMC) and two bulk fill composites (Filtek One Bulk Fill Restorative, BFO; Filtek Bulk Fill Flowable, BFF) were tested, with Filtek Supreme Ultra (FSU) as the control. All tests followed ISO 4049. Beam specimens (25 × 2 × 2 mm, n = 12) were used to determine FS and E after 24 h storage in 37 °C deionized water, using a three-point bending test. Disc specimens (15 × 1 mm, n = 5) were used for WS and SO by measuring mass changes before and after water storage. Data were analysed using one way ANOVA and Tukey post hoc tests (p < 0.05). Results: CCC exhibited the highest FS and lowest WS. BFF showed the lowest E, while BFO exhibited the highest R. FSU demonstrated the lowest FS and R, along with the highest WS. No significant differences in SO were observed among groups. Conclusions: The evaluated materials showed considerable variation in mechanical and physical properties. CCC and BFO demonstrated the most favourable performance, suggesting they are the most suitable candidates for core build up procedures among the materials tested. Full article

The load characteristics of the helicopter tail rotor system are critical to flight safety and handling performance, and flight testing remains the most direct and reliable means to obtain authentic load data. In this paper, the well-established Wheatstone bridge strain measurement method is adopted to carry out accurate load testing on the helicopter tail rotor system. The tail rotor assembly mainly consists of the tail rotor shaft, pitch link, and tail rotor blades, which undertake different load transfer tasks during flight. Under actual operating conditions, the tail rotor shaft bears significant axial tension as well as combined lateral and vertical bending moments; the pitch link is primarily subjected to alternating axial tension and compression; and the tail rotor blades withstand complex loads including flapping bending, lagwise bending, and torsional moments. According to the distinct stress characteristics and force transmission paths of each component, targeted flight test maneuvers are reasonably designed. These maneuvers include steady-level flight at low, medium, and high speeds, zigzag climbing flight, near-ground side-rear flight, as well as deceleration-to-sprint and obstacle slope maneuvers specified in ADS-33E. Key flight parameters are selected for in-depth analysis to reveal the load distribution and dynamic variation patterns of the tail rotor under typical operating conditions. On this basis, a helicopter load risk test point matrix is established to identify high-risk working conditions and key monitoring positions. This study provides a solid theoretical and data foundation for subsequent flight test monitoring and structural strength verification. It effectively reduces flight test risks, improves monitoring efficiency and accuracy, and helps cut down the human, material, and financial costs associated with flight test monitoring. The research results can also provide important references for the design optimization and safety evaluation of helicopter tail rotor systems. Full article

Objectives: Variations in composite resin composition and aging time remain one of the main reasons for replacing esthetic restorations. This in vitro study aimed to evaluate the microtensile bond strength of a low-shrinkage composite resin on a demineralized dentin surface following adhesive interface degradation. Methods: Seventy-eight extracted human molars were prepared, and artificial caries lesions were induced. For microtensile bond strength (μTBS) testing, 60 teeth were randomly assigned to six experimental subgroups (n = 10 per subgroup) based on restorative system and thermal cycling condition. An additional 18 teeth were randomly assigned to six experimental subgroups (n = 3 each) for SEM analysis. Three restorative systems were evaluated, Z250 (conventional resin), K (Kalore resin), and P90 (Filtek P90 resin), each subjected to two thermal cycling conditions: without thermal cycling (NTC) and 12,000 thermal cycles (TC). Results: In the NTC groups, Z250 exhibited a significantly higher bond strength (25.29 ± 10.91 MPa) compared to K (9.69 ± 11.63 MPa) and P90 (9.81 ± 8.49 MPa) (p < 0.05). Following TC, a numerical decrease in bond strength was observed across all groups. Z250 (13.00 ± 10.76 MPa) maintained a significantly higher bond strength compared to K (4.30 ± 6.40 MPa) and P90 (0 ± 0 MPa) (p = 0.001). Notably, the P90 group showed a near-complete loss of bond strength after TC (0 ± 0 MPa), which was a statistically significant reduction compared to its NTC condition (p = 0.002). SEM analysis revealed a predominance of mixed failures in most experimental groups, while the P90 TC group showed a clear predominance of adhesive failures. Conclusions: This study demonstrates that the conventional Bis-GMA resin (Z250) consistently exhibited superior bond strength to demineralized dentin compared to the low-shrinkage resins (Kalore and Filtek P90) under both non-aged and aged conditions. While all materials experienced a reduction in bond strength after thermal cycling, the Filtek P90 system showed a catastrophic loss of adhesion after aging, indicating its particular susceptibility to degradation. These results emphasize the critical roles of resin chemistry and adhesive system selection in long-term bond durability in compromised dentin. Full article

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article

Repurposing decommissioned wind turbine blades provides a vital pathway to mitigate carbon emissions, yet the escalating volume of large-scale waste poses a severe environmental challenge. Recognizing the limitation that existing research focuses predominantly on small-scale legacy blades, this study addresses this gap by assessing the mechanical properties and microstructure of a 54-m (2.0 MW) blade decommissioned due to repowering after 10 years of service. GFRP samples extracted from the root, mid-span, and tip were investigated using X-ray computed tomography and a comprehensive suite of mechanical tests. The investigation confirmed a low internal porosity (~1.2%) without service-induced macroscopic interfacial cracking, alongside superior residual performance, exemplified by a tensile strength of 849.5 MPa at the root. Statistical analysis employing ANOVA revealed significant spatial variations, supporting a graded reuse strategy: roots with superior tensile strengths for critical members, mid-spans for axial compression, and tips as a reliable property baseline for general reuse, while Weibull analysis verified the statistical reliability required for structural design. Based on these superior residual properties, a raft-type wave energy converter utilizing repurposed blade segments was proposed. A comparative carbon footprint assessment revealed that this blade-repurposed WEC achieved a 71.5% reduction in carbon emissions and a 37.4% reduction in structural mass compared to conventional steel counterparts. These findings substantiate the viability of large-scale DWTBs as high-value resources for decarbonizing marine infrastructure within a circular economy. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

To investigate the strength evolution of concrete structures operating in long-term service in humid environments while facing threats such as earthquakes, explosions, and impacts, this study utilized a Hopkinson pressure bar (SHPB) and an MTS testing system to conduct experiments on concrete with four different moisture contents (relative saturation of 0%, 50%, 80%, and 100%) across a strain rate range of approximately 10⁻⁵ to 2 × 10² s⁻¹. Based on these results, a relationship equation was established describing how the strength factor of wet concrete varies with strain rate. The study identified sensitive and non-sensitive regions for the strain rate effect in wet concrete. As the water content increases, the threshold for the sensitive region decreases. Specifically, the inflection strain rate for dried concrete is approximately 32 s⁻¹, whereas for saturated concrete, it drops below 5 s⁻¹. A functional equation describing the variation in the strain rate sensitivity coefficient with water content was derived, showing that the strain rate effect on strength becomes more pronounced as water content increases. The rate-wet coupling effect on concrete compressive strength was analyzed, and zones dominated by the strain rate strengthening effect and the water-weakening effect were identified. The mechanism of strength variation in wet concrete across different strain rate ranges was investigated. The analysis indicates that free water participates in the action processes of each mechanism from low to high strain rates. As the strain rate increases, the mechanisms of pore water interaction and thermal activation undergo a transition. At higher strain rates, the significant increase in the dynamic strength of wet concrete results from the combined and coupled effects of the material’s “true strain rate effect” and the stress wave effect in wet concrete, which are driven by the mutual coupling of pore water, thermal activation, and viscous drag mechanisms. This paper aims to provide a reference for the in-depth understanding of the strength evolution and control of hydraulic concrete structures. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

With the rapid transition of power transmission systems toward higher capacity, longer distance, and improved efficiency, aluminum alloys from the 6xxx (Al–Mg–Si) and 8xxx (Al–Fe) series have become key structural materials for overhead conductors and power cables due to their low density, cost effectiveness, and favorable strength–conductivity balance. Compared with traditional steel-reinforced conductors, optimized aluminum alloy conductors can reduce structural weight by approximately 30–40% and installation cost by about 20–30%, while maintaining comparable current-carrying capacity. This review systematically focuses on modification methods and research progress of aluminum alloy cores for electric wires and cables. The strengthening characteristics of 6xxx alloys (heat-treatment responsiveness and precipitation strengthening) and the creep-resistance stability of 8xxx alloys are comparatively analyzed. Four core performance requirements—high electrical conductivity, mechanical strength, creep resistance, and corrosion resistance—are summarized as evaluation criteria for conductor applications. Particular emphasis is placed on three major modification strategies: (1) microalloying (e.g., Zr, Sc, rare earth elements) for precipitation and dispersoid stabilization; (2) thermomechanical process optimization for grain refinement and strength–conductivity balance; (3) composite reinforcement for high-temperature and ultra-high-strength applications. Quantitative literature data indicate that microalloying and process optimization typically achieve 15–40% strength improvement with conductivity variation within 3–5% IACS, while composite strategies may provide 30–80% strength enhancement but often at the expense of 5–20% conductivity reduction. The distinct applicability of 6xxx and 8xxx alloys under different service conditions is clarified, providing guidance for conductor material selection. Finally, future research directions—including precise composition–process integration, advanced thermomechanical control, and scalable modification technologies—are proposed to support high-performance, cost-effective, and large-scale deployment of aluminum alloy conductors. Full article