Search Results (343)

Iron oxide nanoparticles have emerged as multifunctional compounds with prominent potential in cancer theranostics, particularly in photothermal therapy (PTT) and photodynamic therapy (PDT). Their unique electronic and crystal structures, such as the dispersion of Fe²⁺ and Fe³⁺ ions and d-orbital splitting, contribute to their magnetic and catalytic properties. In PTT, Fe₃O₄ nanoparticles exhibit moderate near-infrared (NIR) absorption and photothermal conversion efficiency, which can be enhanced through adjustments in particle size, surface modification, and combinations with other components. In PDT, Fe₃O₄ nanoparticles demonstrate intrinsic peroxidase-like catalytic activity, facilitating Fenton and photo-Fenton reactions that generate reactive oxygen species (ROS), including hydroxyl radicals (^•OH), thereby amplifying oxidative stress in cancer cells. These nanoparticles can also function as carriers for photosensitisers (PS), promoting targeted delivery and enhanced ROS generation. Multifunctional nanomaterials that integrate Fe₃O₄ with other therapeutic agents and targeting ligands have demonstrated synergistic antitumour effects through amplified photothermal, photodynamic, chemodynamic, and chemotherapeutic mechanisms. Despite certain drawbacks, such as relatively low NIR absorption and challenges in optimising delivery and light activation, ongoing improvements in Fe₃O₄-based nanoplatforms present significant potential for enhancing treatment outcomes and the precision of cancer therapy. This article systematically explores the synergistic role of Fe₃O₄ nanoparticles in PTT and PDT, encompassing their magnetic and catalytic characteristics. Additionally, it focuses on multifunctional hybrid nanoplatforms that combine Fe₃O₄ with targeting or imaging agents, highlighting their potential to enhance therapeutic precision. Full article

Background: Ankle foot orthosis–footwear combination (AFO–FC) tuning involves structured adjustment of the AFO relative to footwear to optimise shank alignment and ground reaction force (GRF) positioning during stance. Although established biomechanical frameworks and clinical algorithms are available, variability in clinical implementation persists. Previous investigations have primarily relied on self-reported practice within single healthcare settings and have not, to our knowledge, systematically examined how orthotists articulate and apply tuning principles within structured clinical reasoning across diverse educational and practice environments. Objectives: This study aimed to determine the level of awareness and framework-based proficiency in AFO–FC tuning among practising orthotists in a geographically diverse convenience sample, to examine the extent to which AFO–FC tuning is integrated into routine clinical practice, and to explore associations between framework-based proficiency level and selected professional characteristics. Methods: A cross-sectional study was conducted using an online survey of practising orthotists (n = 245). Awareness of AFO–FC tuning and self-reported routine implementation were assessed. Framework-based proficiency was evaluated among respondents reporting awareness (n = 212) using structured content analysis of open-text responses within a predefined exploratory five-domain biomechanical framework, and classified as limited (0–1 domains), partial (2–3 domains), or full (4–5 domains). Associations between framework-based proficiency level and professional characteristics were examined using chi-square tests. Binary logistic regression was performed to assess the association between framework-based proficiency level and self-reported routine implementation. Results: Self-reported awareness of AFO–FC tuning was high (86.5%), whereas 53.5% reported routine implementation. Based on the framework scoring, 59.0% demonstrated limited framework-based proficiency, 31.6% partial framework-based proficiency, and 9.4% full framework-based proficiency. No statistically significant associations were observed in this sample between framework-based proficiency level and educational qualification, years of clinical experience, or annual AFO case volume (p > 0.05). Full framework-based proficiency was associated with higher odds of self-reported routine implementation (OR = 4.03, 95% CI 1.44–11.25, p = 0.008). Conclusions: Despite high self-reported awareness, framework-based proficiency in AFO–FC tuning was limited within this sample. Self-reported routine implementation was more frequently reported among respondents with higher framework-based proficiency, whereas no statistically significant associations were observed with educational level, clinical experience, or annual AFO case volume. These hypothesis-generating findings should be interpreted cautiously given the cross-sectional design and framework-based (non-validated) classification. Full article

With growing environmental pressure and tightening resource constraints, artificial intelligence has become a key technical path for urban low-carbon transformation. This study aims to empirically examine whether and how AI-oriented pilot policies affect green economic efficiency (GEE) and identify its underlying mechanisms and boundary conditions. Taking China’s National New-Generation Artificial Intelligence Innovation Development Pilot Zone (NAIDPZ) as a quasi-natural experiment, we use a staggered difference-in-differences model to test the policy effect based on panel data of 267 Chinese prefecture-level cities from 2007 to 2023, with a series of robustness checks to ensure the reliability of the conclusion. We find that the NAIDPZ policy significantly improves urban GEE, with a stronger effect in inland, central, and non-resource-based cities. The composite NAIDPZ policy effect is associated with higher GEE, mainly through green technological innovation and industrial structure optimisation, while its impact is positively moderated by government attention and public environmental attention. These conclusions provide empirical reference for global governments to optimise artificial intelligence policies for low-carbon development. Full article

Dental implants have become common for restoring function and aesthetics after edentulism, with titanium (Ti) remaining the most widely used material due to its excellent mechanical properties and biocompatibility. Despite their clinical success, long-term performance is strongly influenced by surface characteristics, which regulate osseointegration and susceptibility to bacterial colonisation. Consequently, surface modification approaches have become critical strategies to enhance implant stability, bioactivity and longevity. This review critically evaluates conventional, advanced, and hybrid surface modification strategies. Subtractive methods, such as sandblasting and acid etching, increase microroughness (Ra 1.5–3 μm), enhancing osteoblast attachment and differentiation, but may promote bacterial adhesion and surface contamination. Combined treatments like SLA and SLActive generate hierarchical micro–nano topographies, improving protein adsorption, early-stage osteoblast proliferation (up to 2-fold), and clinical stability. Laser ablation and photofunctionalisation further modulate surface chemistry and wettability, accelerating osseointegration and epithelial cell adhesion. Coating approaches, including layer-by-layer self-assembly, nanospray drying, plasma spraying, and piezoelectric nanocomposites, introduce antimicrobial activity (>95% reduction in Escherichia coli or Staphylococcus aureus) and enhanced osteogenic differentiation with mechanical stability, with adhesion values reaching 49 MPa. Hybrid techniques such as sol–gel, hydrothermal, and anodisation provide controlled topography, chemical composition, and bioactivity, promoting early bone-to-implant contact (BIC increase of 10%–25%) in preclinical models. Notwithstanding promising in vitro and in vivo outcomes, variability in processing parameters and limited standardisation restrict large-scale clinical translation. Overall, contemporary Ti surface engineering emphasises a synergistic balance of topography, chemistry, wettability, and hierarchical structuring to optimise biological performance for dental implant applications. Full article

Acute postoperative pain remains a major clinical challenge, affecting both recovery and resource utilisation. Beyond nociceptive input, pain is shaped by cognitive and emotional factors, including patient expectations. This narrative review examines the role of expectations in perioperative pain modulation, framed within predictive coding and Bayesian inference models. These models conceptualise pain as a probabilistic process that integrates sensory input with prior expectations, weighted by precision. In theory, positive expectations may enhance analgesic efficacy, whereas negative expectations may amplify pain via nocebo mechanisms. Control modifies expectations and may reduce perceived pain, while uncertainty diminishes these benefits. Evidence from observational studies links preoperative pain self-efficacy and anticipated pain scores to postoperative outcomes, yet interventional trials remain scarce. In this narrative review, we propose that expectation-sensitive strategies, including structured communication and computational modelling, may inform individualised anaesthesia and analgesia. Future research should validate these frameworks in clinical trials, optimise preoperative expectation management, and explore synergistic approaches that combine pharmacology with cognitive modulation. Understanding and leveraging expectations may offer a promising conceptual direction for more individualised perioperative care, although this approach remains hypothesis-generating at present. Full article

The Draa Sfar polymetallic mine, located near Marrakech in Morocco, represents the deepest currently operating underground mine in North Africa, with workings extending beyond depths of −1200 m. At such depths, mining activities are conducted within weak, highly anisotropic foliated black pelites, where recurrent instability mechanisms, most notably rib buckling and crown deterioration, are frequently observed, especially in drifts developed parallel to the foliation planes. In this context, the present study integrates detailed structural field observations with two-dimensional finite-element modelling using RS2 in order to analyse excavation-scale stability within these schistose pelitic rocks. Both numerical simulations and field evidence indicate that increasing depth-related confinement, together with a dominant in situ stress regime, favours stress channelling and localized damage development, while the pronounced transverse weakness of the pelites exerts a primary control on failure kinematics, including schistosity-parallel spalling, asymmetric rib buckling, and shear along inclined foliation intersecting the excavation back. Instability processes are further intensified by excavation geometry and mine layout: angular, square-shaped profiles and foliation-parallel drift orientations generate steeper stress gradients and greater convergence compared to arched sections, while proximity to stopes and adjacent openings enhances mining-induced stress redistribution and associated deformation. Intersection areas emerge as the most critical configurations, where the superposition of stress perturbations and structurally controlled damage mechanisms accelerates wall convergence and roof sagging. Overall, these findings demonstrate that drift stability cannot be adequately evaluated using generic design criteria when excavation geometry, interaction effects, and structural anisotropy exert a dominant influence on mechanical behaviour. Consequently, a fully integrated approach that combines drift geometry optimisation, detailed structural mapping, site-calibrated numerical modelling, and in situ monitoring is required to achieve reliable stability assessment and control. Full article

Topology optimisation is a powerful methodology for identifying efficient material distributions within prescribed design domains. However, conventional approaches rely heavily on gradient-based optimisation and repeated numerical simulations, which impose significant computational cost and limit their use in early-stage design exploration. This work introduces a generative design framework, referred to as Large Language Model-Driven Symbolic Topology Optimisation (LLM-DSTO), in which large language models act as conceptual design generators. Engineering problems are formulated through structured textual descriptions defining the design domain, boundary conditions, loading scenarios, and material constraints. The language model interprets these inputs and produces symbolic representations of candidate structural topologies. The generated layouts are evaluated using physics-informed objective functions and refined iteratively through lightweight computational procedures. The resulting designs exhibit coherent load paths, strong structural connectivity, and material distributions that are consistent with practical manufacturing requirements, including additive manufacturing constraints. The proposed framework is validated across structural, thermal, thermofluid, and compliant mechanism design problems. Quantitative results show that the generated structures achieve approximately 87.5% of the stiffness obtained using the classical SIMP method for the cantilever benchmark, while reaching about 94.3% of the thermal performance in heat sink optimisation. These results are obtained without repeated finite element simulations, demonstrating a significant reduction in computational cost. In addition, the framework is extended to three-dimensional topology generation, producing volumetric structures under a 50% material volume constraint with coherent internal load paths. Full article

Empty Container Management (ECM) represents a cost-intensive and environmentally impactful component of global container logistics, with its effects most visibly manifested in inland transport systems. Despite extensive academic attention, research on ECM remains fragmented across optimisation, coordination, sustainability, and technology-oriented approaches, often addressing isolated processes or decision problems. As a result, persistent costs, inefficiencies, and emissions continue to characterise inland container logistics. This study applies PRISMA guidelines to systematically review the ECM literature. The analysis focuses on three aspects: the structural causes of container imbalances, the operational activities generating costs and emissions, and the stakeholders influencing ECM decisions. The findings show that empty container imbalances do not arise from a single source. Instead, they result from the interaction of global trade asymmetries, demand uncertainty, fragmented inland operations, and diverse regulatory and institutional environments. The answers to the research questions reveal three fundamental research gaps in the existing literature. First, optimising locally does not always improve the entire system, as it might simply shift costs to other parts of the empty container management (ECM) system. Second, technological solutions cannot operate effectively without appropriate governance mechanisms and data-sharing arrangements. Third, the actors responsible for setting rules and controlling equipment availability often do not bear the full consequences of empty container movements. This review provides a structured foundation for developing integrative decision-support approaches capable of addressing inland ECM under real-world structural constraints. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

The management of large and heterogeneous building stocks requires decision-support tools capable of prioritising interventions under limited technical and financial resources. In this framework, the role of structural deterioration is rarely integrated within a unified prioritisation framework. This study proposes a rapid deterioration-based assessment for prioritising maintenance within heterogenous portfolios. The assessment is articulated into two levels. A Project Level (PL) is based on visual inspections and component-level condition ratings, while a Network Level (NL) introduces contextual and functional modifiers related to the relevance of each structural unit within the building stock. A seismic assessment procedure is integrated in proposed decision-making system for optimising intervention planning. The two assessments are integrated through a decision-tree logic providing an overall classification of buildings within portfolios. The proposed framework is applied to an industrial-oriented building stock located in Italy, comprising 79 structural units characterised by significant typological heterogeneity, including masonry, reinforced concrete, precast reinforced concrete, and steel buildings. The application illustrates the internal consistency of the proposed framework and its ability to support a transparent and articulated prioritisation process for maintenance and risk mitigation within heterogeneous building portfolios. Further applications to different building stocks are required to explore the general applicability of the methodology. Full article

Ensuring the crashworthiness of heavy-duty truck cabs is essential for reducing occupant fatalities and improving passive safety in commercial vehicles. Regulatory frameworks such as UNECE Regulation No. 29 (R29) define structural integrity requirements through full-scale destructive impact tests, which are costly and limit iterative design. In this study, an integrated experimental–numerical methodology is presented for the impact assessment of a real Iveco Eurocargo 120E18 truck cab reconstructed using high-resolution 3D scanning. The scanned geometry was used to generate a dimensionally accurate CAD model of the load-bearing cab structure, which was analysed using explicit finite element simulations in ANSYS Academic Mechanical and CFD Teaching package under impact conditions compliant with UNECE R29 and implemented according to the Swedish regulation VVFS 2003:29. In parallel, a full-scale physical pendulum impact test was performed on the same cab using a cylindrical impactor with a diameter of 580 mm, a length of 1800 mm, and a mass of approximately 1000 kg, impacting the upper region of the A-pillar. The experimental setup was instrumented using high-speed optical measurements and an accelerometer to capture impact kinematics and structural response. The numerical predictions showed good agreement with experimental results in terms of acceleration–time histories, absorbed energy evolution, and structural deformation, with differences generally below 6%. Critical regions susceptible to local buckling and plastic collapse were consistently identified in both approaches, while preservation of the driver survival space was confirmed. The results demonstrate that scan-based finite element models, when properly calibrated and validated, can reliably reproduce certification-level impact behaviour. The proposed workflow provides a robust and cost-effective framework for regulatory pre-validation, structural optimisation, and digitalisation of crashworthiness assessment for heavy-duty truck cabs. Full article

Efficient energy harvesting from open-channel flows offers a sustainable solution for powering distributed sensing systems in water infrastructure. This study investigates a piezoelectric wake-excited membrane vortex-induced vibration (VIV) energy harvester through a combined numerical and mechanical approach. The device features an upstream cylindrical bluff body that generates a periodic vortex street, exciting a downstream flexible membrane equipped with surface-mounted piezoelectric patches. A one-way coupled CFD–FEM framework implemented in ANSYS was employed to assess the effects of membrane length, material stiffness, and flow conditions on hydrodynamic loading, structural deformation, and deformation power. Results show that membrane length mainly affects oscillation amplitude and force levels, whereas material stiffness has a stronger influence on membrane deformation and RMS mechanical power. Among the investigated materials, low-stiffness polyethylene yields the highest deformation power, while none of the analysed configurations reaches a full lock-in condition within the explored parameter range. Complementary mechanical analysis revealed that the stiffness of commercial piezoelectric patches significantly reduces local strain, thereby constraining the practically harvestable energy in the present baseline configuration. Spectral power density analysis identified the dominant shedding frequency and its harmonics, confirming that the flow response is governed by a coherent periodic excitation. These findings highlight key design trade-offs in wake-excited membrane harvesters and provide useful guidance for the future optimisation of self-powered hydraulic monitoring systems. Full article

The Free Energy Principle (FEP) and Active Inference provide a unifying variational framework for modelling perception, action, learning, and self-organisation across biological systems. While highly successful at explaining how systems maintain organisation under uncertainty, these frameworks remain explicitly neutral with respect to a foundational question: why there is experience at all. This paper argues that this limitation reflects not an empirical gap but a misplaced starting point. The Awareness-First Theory (AFT) inverts the usual explanatory order by beginning from the givenness of awareness itself and asking what must be the case for any world to appear coherently. This requirement is formalised as a Coherence Principle, expressed as a variational stationarity condition, $\delta \mathcal{A}=0$, which specifies the invariance of coherent awareness across changing appearances. I argue that familiar variational principles-most notably free-energy minimisation ($\delta F=0$) and stationary-action physics ($\delta S=0$)-can be understood as restricted projections of this parent constraint under specific abstractions. Active Inference therefore does not generate awareness but describes how locally bounded systems maintain coherence within awareness under uncertainty. Making this projection structure explicit dissolves the explanatory gap between physical process and phenomenal presence, revealing the gap itself as a category error. Although the Coherence Principle itself is transcendental rather than empirical, the AFT generates testable consequences at the level of its projections, including predicted dissociations between inferential optimisation and phenomenological coherence in dreaming, altered states, meditation, and psychopathology. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

The integration of Generative AI into civil engineering is currently constrained by the risk of non-compliant outputs and an inherent lack of physics-based knowledge. To address these limitations, this paper presents a conceptual framework for the integration of Agentic Artificial Intelligence (AI) into the complete lifecycle of seismic-resistant structural engineering. The proposal employs a modular software architecture built on the Model Context Protocol (MCP), enabling distributed collaboration among specialised AI agents. We operationalise this architecture across six critical stages, where specific agents govern distinct phases: (1) Seismic Hazard and (2) Structural Modelling agents quantify demands through deterministic tool execution; the (3) Design agent optimises element sizing under the strict governance of Retrieval-Augmented Generation (RAG) for code compliance; (4) Construction Quality Control and (5) Structural Health Monitoring (SHM) agents validate as-built geometry and service-life performance; and an overarching (6) Ethical Audit agent supervises the ecosystem to ensure safety and algorithmic transparency. By decoupling probabilistic design iteration from immutable numerical execution, this framework ensures that generative outputs are traceable, transparent, and professionally accountable, offering a verified pathway for the deployment of AI systems in structural engineering. Full article