Search Results (63,391)

Variable cross-section rotors demonstrate significant potential for enhancing screw vacuum pump performance. This study proposes a variable-radius and variable-pitch screw rotor with a seven-segment fully smooth profile, accompanied by its parametric design methodology. Corresponding clearance design methods are provided, resulting in optimized clearance distribution. A thermodynamic model incorporating four leakage channels was developed. This model effectively simulates screw vacuum pump performance and has been experimentally validated. Systematic analysis was conducted on the effects of key parameters on pump geometric characteristics and performance, with comparative studies against two traditional constant cross-section rotors. Results demonstrate that the proposed design method enables rapid and precise generation of new 3D rotor models. The clearance optimization results validate the design expectations. The variable-radius design achieves cross-sectional variation, and its combination with variable pitch produces a dual internal compression effect, to which the variable radius contributes more significantly. With an increasing cone angle, the internal volume ratio rises significantly. Compared with conventional constant cross-section rotors, the rotor demonstrates superior performance in internal volume ratio, sealing characteristics, and structural integrity, notably cutting shaft power by 52.9% versus equal-pitch rotors. These findings provide an effective solution for developing high-performance screw vacuum pumps. Full article

Background/Objectives: The use of near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) has gained increasing attention in the management of vascular malformations, offering real-time visualization of vascular and lymphatic structures that may improve surgical precision and outcomes. Methods: A systematic review was conducted in accordance with PRISMA guidelines, searching PubMed, Web of Science, CINAHL, and EMBASE databases for studies evaluating the intraoperative use of ICG in vascular malformations, which was prospectively registered in PROSPERO (CRD420251131951). Two independent reviewers screened all records based on predefined eligibility criteria. Extracted data included study design, patient characteristics, ICG administration protocols, clinical applications, and perioperative outcomes. Results: A total of 33 studies comprising 433 patients treated between 2014 and 2025 were included for qualitative synthesis. Nineteen (57.6%) were case reports, seven (21.2%) retrospective descriptive studies, two (6.1%) retrospective comparative studies, three (9.1%) prospective comparative trials, and two (6.1%) prospective descriptive studies. Clinical indications for ICG included capillary and venous malformations (5 studies), arteriovenous malformations (9 studies), and lymphatic malformations (19 studies). Quality assessment with the MINORS tool showed that most studies scored < 17, while only seven reached 18–24, reflecting higher methodological quality. Conclusions: Intraoperative ICG fluorescence imaging represents a promising adjunct in the treatment of vascular malformations, providing real-time visualization that may facilitate lesion delineation, guide resection, and support minimally invasive techniques such as lymphaticovenous anastomosis. However, current evidence is largely descriptive, with very limited comparative outcome data, and high-quality studies are needed to determine whether these technical advantages translate into improved long-term clinical outcomes. Full article

This study focuses on porous polyimide (PPI) lubricating materials for high-speed aerospace bearings. Based on their real microstructure, three-dimensional digital model reconstruction and mesoscale seepage characteristics were investigated. First, a sequence of two-dimensional slice images of PPI was obtained using micro-focus X-ray computed tomography (CT). Through image filtering, threshold segmentation, and three-dimensional reconstruction, a highly faithful digital model of the pore structure was constructed, and a quantified pore-network model was further extracted. Second, a multiple-relaxation-time lattice Boltzmann model based on the D3Q27 discrete scheme was established, and its accuracy and stability in complex boundaries and pressure-driven flows were verified using classic benchmark cases. Subsequently, the validated numerical model was applied to the reconstructed PPI pore structure to simulate and systematically analyze the single-phase seepage behavior of lubricating oil. The results show that the lubricant seepage exhibits a strong “preferential flow path” effect, with most of the flow transported through a small number of large-size throats. A clear quantitative relationship exists between the microscopic flow field structure—including velocity distribution, flow paths, and pressure gradient—and the pore-topology features, such as throat-size distribution, connectivity, and tortuosity. This verifies the mesoscale mechanism that “structure governs flow.” The complete technical chain established in this work—“real-structure reconstruction–numerical model validation–seepage mechanism analysis”—provides a reliable theoretical and numerical tool for gaining deeper insight into the lubricant transport behavior in porous polyimide and offers guidance for the microstructural design and optimization of this material. Full article

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

Stilling basins are critical energy-dissipating structures in high-head hydraulic projects, yet conventional stilling basins often face challenges of insufficient energy dissipation and excessive bottom pressure under high water head and large unit discharge conditions. The integration of sudden expansion and drop sill into stilling basin design has emerged as a potential solution, but its hydraulic characteristics and the specific impact of sudden expansion remain inadequately quantified and understood. To address this research gap, this study experimentally investigates the hydraulic performance of stilling basins with sudden expansion and drop sill, conducting physical model tests on nine design schemes that contrast basins with and without sudden expansion. The tests measure time-averaged pressure, fluctuating pressure, and aeration concentration at key positions of the basin floor. The results demonstrate that the drop sill stilling basin with sudden expansion is technically feasible for application under conditions of high water head and large unit discharge. In the direction perpendicular to the flow, the distributions of time-averaged pressure, fluctuating pressure, and aeration concentration are non-uniform, generally exhibiting a decreasing trend in the order of the 1/4 centerline, chute extension line, 1/2 centerline, and near-sidewall line. Specifically, the time-averaged pressure, fluctuating pressure, and aeration concentration at the bottom of the sudden-expansion basin are, respectively, lower than those of the non-sudden-expansion basin. Notably, the primary protection zones of the sudden-expansion and drop sill stilling basin are situated between the chute extension line and the 1/4 centerline, as well as in the region ranging from the drop sill to 0.4l (with l denoting the stilling basin length). These findings verify that sudden expansion significantly modifies the hydraulic characteristics of stilling basins by reducing pressure and aeration concentration in key areas, and further provide quantitative design parameters and theoretical support for the optimization of sudden-expansion and drop sill stilling basins in high-head hydraulic engineering projects. Full article

The application of artificial intelligence (AI) models in maritime and coastal engineering has gained increasing relevance, demonstrating performance comparable to traditional approaches in wave climate analysis and propagation. However, their use in climate change impact and adaptation studies remains limited, particularly for the design and upgrading of coastal protection structures. To address this gap, this study focuses on the development of an AI-based framework to support the adaptation of breakwaters to future climate conditions. A hybrid approach combining artificial neural networks (ANNs) and genetic algorithms (GAs) was implemented, with two feedforward neural networks-based models developed and applied to different sections of the north breakwater of the Port of Valencia, specifically a vertical section and a compound breakwater. The results indicate that, under future climate scenarios (2050), increases of up to 1.2 m in crest elevation, together with reinforcement of the armor layer, are required to ensure adequate structural performance. The analysis also highlights the critical role of extreme events, as approximately 60% of the model errors were concentrated in the upper 90th percentile of wave conditions. Overall, the proposed hybrid ANN-GA framework demonstrated very strong performance, achieving computational efficiencies 30 to 40 times greater than ANN-only models in terms of computational time. These findings underscore the necessity of adapting coastal structures to climate change and confirm the potential of AI-based models as effective tools for climate-resilient coastal engineering, while emphasizing the importance of accurately representing extreme wave conditions. Full article

Solar energy, as a clean and renewable resource, plays a pivotal role in advancing sustainable energy technologies. The efficiency of front-side silver paste is critical for the photovoltaic performance of Tunnel Oxide Passivated Contact (TOPCon) solar cells. In this study, we comprehensively investigated how the composition of organic vehicles in conductive pastes influences both printing rheological properties and electrical performance. Through rheological characterization, contact angle measurements, and Three-Interval Thixotropy Tests (3ITT), we examined the effects of varying solvent, binder, and thixotropic agent ratios on paste properties. The optimized formulation—a solvent mixture of lauryl alcohol ester (TE), butyl carbitol (DGME), butyl carbitol acetate (BCA), and dibutyl phthalate (DBP) in a 3:4:2:1 ratio, with ethyl cellulose (EC) STD10 as the binder and a polyamide wax (PAW)–hydrogenated castor oil (HCO) thixotropic agent at a 3:1 mass ratio—demonstrated superior viscosity control and rapid structural recovery. Printed grid lines achieved a height-to-width ratio (H/W) of 0.35 and a sheet resistance (Rs) of 1.43 Ω/□. These findings reveal direct relationships between organic vehicle composition, paste rheology, and functional performance, providing practical guidance for the design and optimization of high-performance conductive pastes for c-Si solar cells. This work establishes a foundation for improving both the efficiency and reliability of next-generation silver paste formulations in photovoltaic applications. Full article

Following the growing interest in small-scale unmanned aerial vehicles (UAVs), this paper presents a comprehensive conceptual design methodology for a modular ducted-fan aerial vehicle intended for research applications. Although ducted-fan configurations offer significant advantages over conventional multirotor platforms, particularly in urban, indoor, and human-interaction scenarios, the availability of affordable and customizable ducted-fan UAVs platforms suitable for scientific research remains limited. To address this gap, the paper details the complete design of the vehicle, including propeller aerodynamics and duct design, mechanical structure, actuation system, dynamic modeling, and control strategy. All major structural and aerodynamic components are fabricated using low-cost additive manufacturing, enabling rapid prototyping and high modularity. The vehicle’s performance is experimentally assessed through bench tests and indoor flight experiments, demonstrating stable flight and satisfactory attitude control. The presented work shows that a fully functional ducted-fan UAVs can be realized using commercial off-the-shelf electronics and exclusively 3D-printed components, and provides practical guidelines to replicate and adapt the proposed platform for advanced research in UAVs control, navigation, and autonomy. Full article

High-strength concrete (HPC/UHPC) provides a new way to optimize the performance of nuclear power containment, but the mechanism of its application in the bi-directional dense anchorage zone is not clear. In this paper, a three-dimensional nonlinear model was established by ABAQUS to systematically study the effects of concrete strength, cylinder thickness and other parameters on the local pressure-bearing performance of the anchorage zone. The study shows that the use of C80-C120 concrete can thin the thickness of the containment by 30–40% and significantly enhance the pre-compressive stress safety reserve. Comparison of the existing design codes (e.g., Kim’s formula, Highway Bridge Specifications and UHPC-related regulations) reveals that the prediction results have a non-conservative or over-conservative tendency, which restricts the full utilization of the material performance. This study reveals the working mechanism of bi-directional multiple anchorage zones, demonstrates the advantages of high-strength concrete containment in terms of safety and economy, and provides a theoretical basis for the design of advanced nuclear power structures. Full article

Recently, the United States unveiled a conceptual design of an unmanned high-speed vehicle, the SR-72, which boasts a maximum flight speed of Mach 6, enabling rapid airspace dominance and superior combat performance. To this end, this study conducted a comprehensive review of publicly available data and employed 3D modeling software to reconstruct the SR-72 configuration, utilizing the supersonic thin airfoil NACA 16006 for the wing design. Subsequently, a meticulously structured computational mesh was generated. Numerical simulations were conducted across subsonic, transonic, supersonic, and high-Mach-number flow regimes. The results reveal that the vehicle exhibits high maneuverability in subsonic conditions, with a stall angle of attack reaching 24°. In transonic conditions, significant wave drag is observed, while, in supersonic and high-Mach-number flow regimes at Mach 6, the vehicle demonstrates excellent wave-riding performance, enabling extended cruise durations and improved fuel efficiency. Furthermore, the initial airfoil was optimized using the CST (Class-Shape Transformation) parameterization method and the SLSQP (Sequential Least Squares Programming) algorithm. Under the given constraints, the drag coefficient was reduced by 40%, demonstrating a significant optimization effect. Full article

This study examines the impact of integrating Generative Artificial Intelligence (AIgen) and Augmented Reality (AR) within a Project-Based Learning (PBL) framework to enhance student motivation and active learning in Chemical Engineering Unit Operations (OU) courses. The research employed a quasi-experimental post-test design without a control group and followed a mixed-methods explanatory approach. The intervention was implemented over two academic semesters at the Universidad Técnica Particular de Loja. The instructional design was structured using the 4PADAFE model, based on the principles of constructive alignment and competency-based education. The intervention was organized into seven sequential phases that integrated pedagogical planning, technological implementation, and evaluation. Students collaboratively developed educational resources, including interactive AR visualizations and AI-generated teaching materials addressing key topics such as fluid flow, heat transfer, and adsorption kinetics. The quantitative component constituted the main analytical approach and assessed changes in motivation using the Instructional Materials Motivation Survey (IMMS), based on Keller’s ARCS model. The instrument’s reliability was supported by high internal consistency (Cronbach’s α > 0.95) and satisfactory factor adequacy indices. The results revealed increases in all motivational dimensions, with mean scores rising from 3.26–3.33 in Phase I to 3.48–3.60 in Phase II. Overall, the findings indicate that the structured integration of AIgen and AR within an aligned problem-based learning (PBL) framework was associated with improvements in student engagement, confidence, and satisfaction. The study provides evaluative evidence supporting the implementation of technology-enhanced teaching strategies in engineering education under real-world classroom conditions. Full article

Introduction: The biological effects of dietary polyphenols have gained increasing attention due to their roles in regulating oxidative stress, inflammatory processes, and mitochondrial function. Human studies suggest that polyphenol intake may support aspects of post-exercise recovery, neuromuscular function, and selected aspects of physical performance. However, most investigations have been conducted in young or metabolically healthy populations, limiting direct clinical translation to older adults. Objective: This narrative review aims to synthesize current mechanistic and human evidence on the physiological and recovery-related effects of dietary polyphenols in the context of exercise adaptation and skeletal muscle function, and to examine their potential relevance to muscle aging and sarcopenia. Methods: A structured, non-systematic literature search was conducted to integrate findings from human intervention trials, preclinical studies, and mechanistic research addressing polyphenols, exercise adaptation, muscle recovery, and muscle aging. Evidence was synthesized narratively with emphasis on shared physiological pathways and functional outcomes. Results: Human intervention studies suggest that polyphenol intake may attenuate biomarkers of exercise-induced muscle damage, modulate inflammatory responses, and accelerate recovery of muscle strength and functional performance. Mechanistic evidence supports the involvement of redox homeostasis, mitochondrial regulation, and inflammatory signaling as central mediators of these effects. While clinical data in older populations remain limited, converging evidence suggests biological overlap between recovery-related pathways and mechanisms implicated in age-related muscle decline. Conclusions: Current evidence is consistent with a biologically plausible role for polyphenols in modulating exercise-related physiological and recovery processes. By aligning recovery-focused evidence with pathways central to muscle aging, this review proposes a translational framework that may inform the design of future targeted clinical trials in older and clinical populations. Full article

Conventional food security strategies have largely been formulated under assumptions of population growth, abundant agricultural labor, and stable global trade. However, many advanced economies—particularly in East Asia—are entering a shrinking-society context characterized by population decline, rapid aging, and regional depopulation. This paper argues that demographic shrinkage should be understood not as a peripheral trend but as a landscape-level structural pressure that destabilizes incumbent agri-food systems. Drawing on the Multi-Level Perspective (MLP), the study conceptualizes demographic shrinkage as a cumulative force that erodes the labor base, productive capacity, and institutional stability of food systems, thereby weakening regime path dependence. Building on this framework, it advances Food Security 3.0 as a theory-driven contribution to sustainability research. Food Security 3.0 reconceptualizes food security under shrinkage conditions as a problem of systemic resilience rather than production expansion or import diversification, and theorizes food technology—including smart and automated agriculture, alternative proteins, and AI-enabled supply chains—as transitional infrastructure enabling regime reconfiguration under structural constraints. By integrating demographic change, socio-technical transitions, and governance, the study reframes food security as a question of resilience-oriented system design, strategic self-reliance, and integrated food-system governance. While anchored in the East Asian experience, the framework offers theoretical and policy-relevant insights for shrinking societies confronting overlapping demographic, climatic, and geopolitical pressures. Full article

Nanoparticle-based strategies have emerged as a versatile and powerful approach for cancer therapy, enabling the integration of material science, molecular biology, and immunology into multifunctional therapeutic platforms. Over the past decade, significant advances in nanoparticle design have expanded their potential beyond passive drug carriers toward systems capable of active targeting, microenvironment-responsive behavior, and immune modulation. This review provides a comprehensive and up-to-date overview of the major nanoparticle platforms developed for cancer treatment, including lipid-based, polymeric, inorganic, and bioinspired nanomaterials, with particular emphasis on their structure–property relationships and biological interactions. We discuss key targeting strategies, spanning passive, active, stimuli-responsive, and cellular or immune-mediated approaches, and analyze how nanoparticles can overcome biological barriers imposed by the tumor microenvironment, such as abnormal vasculature, dense extracellular matrix, hypoxia, and immunosuppression. Special attention is given to nanoparticle-enabled cancer immunotherapy, including vaccine delivery, mRNA–lipid nanoparticle systems, and combination strategies that integrate immunotherapy with conventional treatments. Finally, we critically examine safety, toxicity, and translational challenges that continue to limit the clinical impact of cancer nanomedicine, highlighting the importance of biologically informed design, manufacturing robustness, and regulatory considerations. By synthesizing current advances and identifying emerging trends, this review aims to provide a framework for the rational development of next-generation nanoparticle-based cancer therapies with improved clinical relevance. Full article

With the rapid advancement of agricultural modernization, ensuring production safety has become a pressing concern, yet the mechanisms through which government regulation fosters safe production remain underexplored. This study addresses this gap using a two-stage survey design: first, panel-tracked survey data collected from 2021 to 2024 are used to document the evolution of regulatory perceptions among agricultural enterprises; second, a cross-sectional analytical design based on three survey waves conducted in 2024 is employed to examine the effect mechanisms using structural equation modeling method. Drawing on survey data from 485 Chinese agricultural enterprises in 2024, the findings show that four regulatory types—normative, punitive, incentive, and service—promote safe production both directly and indirectly through dual pathways: knowledge acquisition (cognitive–technical capacity building) and risk awareness (preventive attitudinal orientation). Mediation comparison analysis reveals that these two mechanisms exert equivalent effects across all regulatory pathways, indicating complementary rather than competing roles. Theoretically, the study advances regulatory pluralism and dual-mediation frameworks in organizational safety research; practically, it offers guidance for policymakers to design integrated regulatory portfolios and for managers to strengthen both knowledge systems and risk-aware cultures. Full article