Search Results (22,321)

The study investigates the influence of Ca addition (0, 0.5, 0.7, and 1 wt.%) on the microstructure and mechanical properties of friction stir-processed (FSPed) Mg-2.5Si-4Zn-xCa alloys. The microstructures of the processed alloys were characterized using OM, SEM, EBSD, and TEM. The results indicated that Ca addition combined with FSP can synergistically refine and homogenize the Mg₂Si phase, and with increasing Ca content, the size of both primary and eutectic Mg₂Si phases first decreases and then increases, reaching an optimum refinement at 0.7 wt.% Ca. In this composition, the Mg₂Si phases were uniformly dispersed, and the stir zone exhibited significantly refined recrystallized grains compared to its Ca-free counterpart. Under identical FSP conditions, the Mg₂Si phases in the Ca-containing alloys underwent a higher degree of fragmentation. The addition of Ca promoted the formation of the CaMgSi phase by enriching Ca atoms at or near the Mg₂Si phases during FSP, which further assisted in fragmenting the Mg₂Si phases. Consequently, the alloy with 0.7 wt.% Ca demonstrated the best mechanical properties at both room and elevated temperatures, exhibiting tensile strength and elongation of 276.57 MPa and 11.60% at room temperature, 190.13 MPa and 12.17% at 150 °C, and 133.43 MPa and 15.96% at 200 °C, respectively. Full article

Conventional concrete masonry construction consists of an assemblage of concrete blocks, mortar, grout, and steel reinforcement. While effective, this constructive method is constrained by its low productivity. In recent decades, advances in construction and manufacturing technologies now allow for the production of larger and more complex block typologies, enabling designers to reassess conventional designs to optimize structural performance and construction efficiency. As such, this study introduces the “mega-interlocking block”, a novel block that integrates the benefits of mega blocks (i.e., blocks with larger sizes) with a newly designed interlocking mechanism to enhance structural performance and expedite the construction of masonry walls in work sites where forklifts, scissor lifts and other smaller crane equipment are available. A numerical study was conducted to evaluate the in-plane (IP) and out-of-plane (OOP) behaviors of masonry walls constructed with mega-interlocking blocks, including both unreinforced masonry (URM) and reinforced masonry (RM) configurations, compared to standard block walls. A simplified micro-modeling approach was utilized to account for various possible failure modes associated with masonry structures. Results indicate that mega-interlocking blocks significantly improve wall stiffness and load-bearing capacity under IP loading, both with and without mortar, outperforming standard block walls. Under OOP loading, interlocking blocks provide moderate performance gains when mortar is present, though their effectiveness diminishes in mortarless configurations. For URM walls under IP loading, the implementation of mega-interlocking blocks yielded substantial improvements in stiffness and capacity, with the most notable benefits observed in walls with larger aspect ratios. Although the relative advantages in RM walls were less pronounced due to the homogenizing effects of grout and reinforcement, mega-interlocking blocks still demonstrated robust structural performance, making them a promising alternative to standard masonry units. Full article

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

Traditional social recommendation methods often focus on static representations of users and items, neglecting dynamic changes in user interests and item attractiveness over time, which makes it challenging to adapt to temporal variations in user interests. Additionally, the propagation of information along explicit social relationships tends to over-smooth features and weaken individual preferences, while static implicit relationships may increase short-term noise. Thus, a Dual-channel Controllable Diffusion Network based on Hybrid Representations (HR-DCDN) is proposed for social recommendation. The HR-DCDN first incorporates temporal factors by combining dynamic and static representations to capture changes in user interests and item attractiveness. Then, our method proposes a dual-channel aggregation mechanism to obtain higher-order representations of users and items. Explicit social relationships serve as the social-influence channel, while implicit social relationships discovered via dynamic implicit relationship mining constitute the preference-homophily channel. In addition, a learnable polynomial spectral filter incorporates residual connections and dual-channel fusion information at each propagation step, stabilizing deep propagation and alleviating representation homogenization to a limited extent while preserving high-frequency preference information. Finally, we jointly optimize a cross-layer InfoNCE objective on the perturbed interaction branch with the supervised rating loss, which provides an additional empirical regularization effect, improves robustness, and helps preserve representation diversity without altering the graph structure. Experimental results demonstrate that our model outperforms baseline methods on two real-life social datasets. Full article

Gas-filled discharge capillaries are widely used in the field of plasma-based particle accelerators, due to their compactness, cost-effectiveness and versatility for different applications. Technological improvement of such plasma sources is necessary to enable high energy gain acceleration at the meter scale, as required for next-generation particle colliders and light sources. Beam quality preservation within such an acceleration length involves accurate tuning of the plasma properties. In particular, precise tailoring of the plasma density distribution is required to control the emittance growth of particle bunches during the acceleration process. In this context, this paper presents a scalable and versatile approach for the design of meter-scale discharge capillaries, aimed at achieving fine tuning of the plasma density distribution, with the possibility of locally controlling the density profile by acting on the source geometry. Forty-centimeter-long capillaries are designed using numerical fluid dynamics simulations and tested in a dedicated plasma module. Different arrangements of the gas inlets are tested, with their number and diameter varied, to assess the effect of the capillary geometry on the plasma properties. Plasma density measurements show that a higher number of inlets with variable diameter along the plasma formation channel provides an enhancement in the homogeneity of the electron plasma density distribution. Longitudinal density plateaus are observed along most of the plasma channel length, with a center-to-end density uniformity of up to 80%. The experimental results highlight the proposed approach’s capability to modulate the longitudinal plasma density distribution by acting on the capillary geometry, thus providing uniform density profiles over the meter scale, as required for plasma-based acceleration experiments. Full article

This study presents a comprehensive comparative analysis of the performance, combustion, and emission characteristics of a single-cylinder, four-stroke spark-ignition engine fueled by commercial gasoline and raw biogas enhanced with cerium oxide (CeO₂) nanoparticles. Raw biogas containing 58% methane was tested without carbon dioxide removal to reflect practical rural applications, while CeO₂ nanoparticles were ultrasonically dispersed in the fuel to promote homogeneous suspension and catalytic activity. Experiments were conducted under wide-open and part-throttle conditions across a range of engine speeds, with simultaneous measurement of brake thermal efficiency, brake-specific fuel consumption, volumetric efficiency, in-cylinder pressure, heat release rate, combustion phasing, and regulated emissions. The results showed that while gasoline consistently outperformed biogas in torque and power due to its higher heating value and flame speed, the addition of CeO₂ significantly reduced the performance gap. For the biogas mode, CeO₂ addition increased brake thermal efficiency by up to 5%, lowered brake-specific fuel consumption by up to 8%, and shifted the start of main combustion to earlier crank angles, indicating faster and more complete combustion, particularly at high loads where higher temperatures activate CeO₂’s catalytic behavior. Emission analysis revealed that CeO₂-blended biogas reduced carbon monoxide emissions by approximately 25% and unburned hydrocarbons by up to 55% compared with gasoline, while nitrogen oxide emissions were consistently 15–22% lower. These reductions were observed across both wide-open and part-throttle conditions, confirming improved combustion completeness and lower peak flame temperatures. These improvements are attributed to CeO₂’s oxygen-storage capability, catalytic oxidation activity, and enhanced thermal conductivity, which collectively strengthen combustion completeness and cyclic stability. The findings demonstrate that nanoparticle-enhanced biogas can substantially improve the environmental and operational viability of spark-ignition engines, offering a practical pathway for integrating renewable gaseous fuels into existing transportation systems. Full article

Stainless steel (SS) remains widely used in orthopedic implants but is susceptible to corrosion and implant-associated infections in physiological environments. This study aimed to develop a multifunctional multilayer coating combining corrosion resistance, bioactivity, and antimicrobial performance. A ZnO base layer was deposited on 316L SS via pulsed laser deposition, followed by matrix-assisted pulsed laser evaporation of a lovastatin-functionalized bioactive glass (BG57 + LOV) top layer. Two LOV concentrations were initially evaluated, and BG57+0.1LOV was selected based on structural homogeneity, cytocompatibility, and antimicrobial balance. Physicochemical characterization confirmed preservation of chemical integrity and formation of continuous, moderately rough coatings. Electrochemical impedance spectroscopy in simulated body fluid demonstrated progressive improvement in corrosion resistance from bare SS to ZnO-coated and finally to the BG57+0.1LOV/ZnO multilayer, which exhibited the most electropositive corrosion potential and effective suppression of charge-transfer reactions. Biological assays revealed high viability of osteoblasts, fibroblasts, keratinocytes, and macrophages without significant oxidative or nitrosative stress. Antimicrobial testing showed strain-dependent activity, with enhanced efficacy against MRSA and significant reduction in P. aeruginosa, associated with increased ROS/RNS generation. Overall, the BG57+0.1LOV/ZnO system represents a promising multifunctional coating strategy for corrosion-resistant and infection-resistant SS implants. Full article

A microalloyed high-carbon low-alloy steel was designed to clarify the combined effects of austenitizing temperature and deep cryogenic treatment (DCT) on microstructural evolution and mechanical performance. Specimens were austenitized at 770–900 °C, water-quenched, subjected to DCT at −196 °C, and subsequently tempered at 180 °C. Microstructural characterization by XRD, EBSD, and TEM indicates that the quenched microstructure is dominated by martensite and cementite, with retained austenite below 1% at moderate austenitizing temperatures. DCT does not fundamentally alter the martensitic morphology but promotes the transformation of retained austenite and induces substructure fragmentation, dislocation reorganization, and a more homogeneous lattice strain distribution. Concurrently, carbon redistribution during cryogenic exposure facilitates the formation of finely dispersed carbides. After tempering, partial recovery and stabilization of the martensitic substructure lead to reduced lattice distortion while maintaining a high density of effective strengthening features. Mechanical testing shows that DCT combined with appropriate austenitizing (770–790 °C) improves hardness and ultimate tensile strength with acceptable ductility, whereas excessive austenitizing at 900 °C results in severe grain coarsening and intergranular brittle fracture. The results demonstrate that optimized integration of microalloying and DCT enables a favorable strength–toughness balance in high-carbon tool steels. Full article

The bioactive compounds in thyme essential oil (TEO) have been investigated as natural preservatives. However, their direct application in foods is limited by their poor water solubility and high volatility. In this context, nanoemulsions represent promising delivery systems for bioactive compounds due to their improved physicochemical stability and functional performance. This study aimed to develop and characterize TEO nanoemulsions prepared by ultrasound-assisted encapsulation using an ultrasonic probe and whey protein concentrate as a surfactant, with potential application in chicken burgers. Different sonication times (1, 3, 5, 7, and 10 min) were evaluated, and ultrasonication time was evaluated as the experimental variable. The formulation processed for 3 min presented the smallest hydrodynamic diameter (289 nm) and a homogeneous spherical morphology. The nanoemulsions showed low cytotoxicity, maintaining cell viability above 90% at all evaluated concentrations. In vitro antibacterial assays demonstrated activity against Staphylococcus aureus and antifungal effects against Aspergillus and Penicillium species. When applied to chicken burgers, the treatment containing 100 ppm of nanoencapsulated TEO contributed to reductions in S. aureus and mesophilic aerobic microorganism counts during 7 days of refrigerated storage. These findings indicate that TEO nanoemulsions present potential as natural antimicrobial systems for food preservation applications. Full article

This comparative study investigates binder-free binary WC-TiC and ternary WC-TiC-TaC carbide ceramics as alternatives to cobalt-bonded hard materials. Equimolar compositions were processed via high-energy ball milling (HEBM) and consolidated by spark plasma sintering (SPS) at 1700–2100 °C. X-ray diffraction analysis (XRD) revealed fundamentally different homogenization kinetics: the ternary system achieved a complete single-phase structure at 2000 °C, 100 °C earlier than the binary system. This acceleration correlates with finer initial particle size (2–5 μm vs. 3–10 μm) and near-stoichiometric TaC, facilitating interdiffusion. Lattice parameter evolution confirmed the formation of (W,Ti)C and (W,Ti,Ta)C substitutional solid solutions. Mechanical characterization showed contrasting behaviors: binary WC-TiC exhibits maximum hardness at 1900 °C (1793 HV30, fracture toughness 5.07 MPa·m^1/2), while ternary WC-TiC-TaC peaks at 1700–1800 °C (1947–1782 HV30) with higher toughness (max 5.42 MPa·m^1/2). Optimal processing windows with acceptable property uniformity are 1800–1900 °C (binary) and 1700–1900 °C (ternary). The binary system offers superior toughness and stability; the ternary system enables faster processing and higher initial hardness, defining distinct application domains. Full article

Plunge shaving is a widely used finishing process for high-precision gears due to its high productivity and cost-effectiveness. However, manufacturing the plunge-shaving cutter itself remains challenging, particularly for modified tooth profiles. Because the theoretical cutter flank exhibits a hyperboloid-like geometry in the lead direction, conventional disk-wheel grinding tends to introduce systematic twist-like topographic bias. To overcome this limitation, a comprehensive mathematical framework is developed for the generative grinding of plunge-shaving cutters using an involute-helicoid grinding worm. Based on envelope theory and homogeneous coordinate transformations, the theoretical cutter surface is first derived, followed by the establishment of a complete kinematic grinding model. A linear least-squares optimization algorithm is then formulated to determine the optimal center-distance compensation parameter for minimizing the normal deviation between the generated and theoretical surfaces. Numerical simulations demonstrate that the proposed method significantly suppresses twist-related topographic errors. In a benchmark moderate-helix case, the maximum residual deviation is controlled to approximately 2 µm. For a more demanding large-helix configuration, a two-level optimization strategy—combining machine-setting compensation and grinding-worm helix-angle adjustment—reduces the peak deviation from about 5.5 µm to 4.7 µm, corresponding to an improvement of approximately 15%. This confirms that worm-geometry tuning provides an additional, effective degree of freedom for high-helix cutter applications. Full article

The NA64 experiment at the CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfill the energy resolution requirement of the physics measurement ${\sigma }_E/E\simeq 2.5\%/\sqrt{E\left[\mathrm{GeV}\right]}\oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO₄ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The R&D program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO, aiming at demonstrating that the obtained performances are compatible with the application requirements. Full article

Background: Breast cancer brain metastases (BCBM) lack effective treatments, contributing to breast cancer-related morbidity and mortality. Integrating translational animal models and advanced non-invasive imaging can accelerate the development of urgently needed therapies. Method: In this study, we developed an intracarotid method mimicking BCBM and compared it to the stereotactic model in terms of animal welfare, tumour establishment, and blood–brain barrier (BBB) permeability. BCBM was established through intracarotid or stereotactic inoculation of BT474 and MDA-MB-231.Luc2 cells in NMRI nude mice. We utilised magnetic resonance imaging (MRI) and bioluminescence imaging (BLI) to monitor tumour growth and BBB permeability, supported by fluorescent immunohistochemistry for validation. Finally, light sheet microscopy (LSM) was employed to visualise tumour establishment in intact brains. Results: Both inoculation methods achieved a survival rate >70%, with animals recovering within a week post-surgery. MRI and BLI effectively visualised tumour growth with stereotactic implantation, resulting in single tumours, while intracarotid inoculation led to micro-seeding of up to seven tumours in one brain. Tumour growth was rapid and homogenous in the stereotactic model, whereas the intracarotid model exhibited slower, heterogenous growth. Notably, BBB permeability was significantly higher in small tumours in the stereotactic model when compared to the intracarotid model (p = 0.003). Ex vivo analyses validated these findings with the identification of multiple metastasis in the intracarotid model and single tumours in the stereotactic model. Conclusion: We developed an animal model that closely mimics BCBM, highlighting extravasation and micro-seeding while maintaining animal welfare. Our established imaging protocols enable longitudinal evaluations of BBB permeability and treatment response, creating a translational platform for testing novel anti-cancer therapies. Full article

To address the challenges of homogenization and unclear functional hierarchy in pet water fountain design, as well as to meet diverse user needs, reduce costs, and improve efficiency, this study undertakes product design based on comprehensive research and analysis of key design elements that fulfill the practical requirements of both humans and pets. Furthermore, to evaluate and optimize the proposed design scheme, an integrated AHP-improved CRITIC-TOPSIS comprehensive design evaluation model is introduced within the framework of multi-criteria decision theory to assess and refine pet water fountain design solutions. The methodology commences with the application of the AHP to construct a multi-level evaluation index system and determine subjective weights for each index. Subsequently, the improved CRITIC method is integrated to calculate the comprehensive weights of each indicator. The TOPSIS method is then employed to rank and optimize the design schemes. Strategies for further improvement are proposed based on key indicators that are assigned higher weights. The results of the simulation verification experiment and sensitivity analysis indicate that the proposed method achieves high accuracy and reliability in the evaluation of pet water fountain designs. This methodology establishes a rigorous evaluation framework that can be extended to other pet product designs. Full article

Monitoring forest density is essential for understanding ecosystem health, wildfire risk, and post-disturbance recovery. This study proposes a robust methodology to extract forest density classes exclusively using Sentinel-2 multispectral imagery combined with vegetation indices (VIs), textural parameters, and spatial clustering metrics. The approach was applied to the northern part of Euboea Island, Greece, as a pilot area severely affected by a wildfire in August 2021. Four cloud-free Sentinel-2 images (2017–2024) were selected to capture pre- and post-fire conditions. A set of nine VIs—representing vegetation vigor, chlorophyll content, soil exposure, and canopy moisture—were calculated and statistically assessed for independence. To enhance classification accuracy, texture measures (homogeneity, correlation, and entropy) and spatial autocorrelation metrics (Moran’s I, Getis-Ord Gi) were derived for selected VIs. Supervised classification was performed using the Maximum Likelihood algorithm, yielding overall accuracies up to 89.4% and kappa coefficients above 0.85 when combining VIs with texture and spatial metrics. Results revealed a dramatic 49.3% reduction in forest cover immediately after the wildfire, with partial recovery (to 77.9% of pre-fire levels) three years later, mainly as a low-density forest. Approximately 12.1% of forest cover failed to regenerate, indicating potential long-term ecosystem degradation. The proposed approach provides a computationally efficient, high-accuracy alternative to data-fusion methods involving (Light Detection and Ranging) LiDAR or (Synthetic Aperture Radar) SAR datasets, making it suitable for operational forest monitoring and fire-risk management. Full article