Search Results (51,369)

Cold recycled asphalt mixtures incorporate a high amount of reclaimed asphalt pavement (RAP), which offers more economic and environmental advantages than hot recycling techniques. Nevertheless, the presence of aged RAP binder frequently leads to reduced low-temperature performance and uncertainty in mechanical response. The influence of slack wax on full-depth reclamation (FDR) mixtures with bitumen emulsion is assessed in this study using a dual-scale approach. The approach integrates both chemical and rheological binder-scale characterization with mixture-scale mechanical performance with variability assessment. At the binder scale, the binder beam rheometer (BBR), dynamic shear rheometer (DSR), and Fourier transform spectroscopy (FTIR) indicated that the addition of 10% recycling agent improved the low-temperature properties. The improvement at lower temperatures shifted the BBR temperature from −23 °C to −30 °C, which ultimately resulted in a less negative ΔT_c, from −0.7 °C to −0.3 °C, and moderately improved high-temperature stiffness. Moreover, the FTIR analysis indicated a reduction in oxidation-related chemical markers, as evidenced by the reduced carbonyl and sulfoxide indices. At the mixture scale, complex modulus shows a systematic decrease in stiffness, particularly at lower temperatures of −25 °C and −15 °C, and a reduced phase angle, suggesting higher elastic dominance. The reduction is observed at all temperatures and frequencies. Rutting resistance of both formulations remains below 3% after 30,000 cycles. The complex modulus coefficient of variability was found to be 8–12%, comparable to that of hot mix asphalt. In conclusion, the findings suggest that the recycling agent provides a controlled restoration of viscoelastic properties in cold recycled mixtures without compromising structural integrity. This underscores the significance of multi-scale evaluation and variability assessment when characterizing high RAP recycling agents under the studied materials and dosage. Full article

We formulate a structural stability criterion for dimensionless physical constants within standard perturbative field frameworks. The analysis introduces a response-ratio functional $\mathit{\Gamma }=\kappa /\tau$, defined from second-order sensitivity and first-order deformation measures associated with admissible variations in a field configuration. Stability is characterized by proportional stationarity of $\mathit{\Gamma }$, expressed as a first-order operator condition along transformation flows. The framework characterizes, within a declared variational model, when invariance of fixed constants can be represented as a stationarity condition. Under compactness and convexity assumptions typical of variational systems, stationary response ratios arise as isolated solutions of the associated operator equation; more general settings permit continuous spectra. Explicit functional definitions are provided within a conventional analytic setting, and the criterion is illustrated in representative classical field models. The results position proportional stationarity as a model-relative structural consistency condition for perturbative stability; isolation is conditional on compactness and non-degeneracy hypotheses, and continuous families may occur outside that regime. Limitations and possible extensions, including discretized spacetime formulations, are discussed. Full article

Background: Skin repair and skin wound healing are tightly regulated biological processes that require coordinated control of inflammation, redox homeostasis, angiogenesis, and tissue remodelling. In this context, natural extracts are increasingly recognized as sources of chemically diverse phytochemicals capable of modulating defined molecular signalling pathways that govern cutaneous repair. Methods: This review provides a mechanism-informed synthesis of current evidence by examining representative botanical sources, including Aloe vera, Centella asiatica, Curcuma longa, Calendula officinalis, and Panax ginseng, which have been extensively investigated in preclinical wound-healing models. Rather than providing an exhaustive catalogue of plant species or individual compounds, the analysis emphasizes how distinct phytochemical classes interact with conserved molecular pathways involved in skin repair. Results: Flavonoids, terpenoids, phenolic acids, alkaloids, and polysaccharides influence inflammatory signalling pathways, redox-sensitive pathways, growth factor-mediated responses, and cellular migration, thereby supporting phase-appropriate progression of wound healing. Recurrent modulation of NF-κB, TGF-β, VEGF, and Nrf2 signalling emerges as a central mechanistic theme. Advances in dermopharmaceutical formulation strategies, including hydrogels and lipid-based carriers, may enhance local delivery and stability of phytochemicals; however, their translational value remains dependent on chemical standardization and mechanistic validation. Conclusions: This review provides a mechanism-informed synthesis of current evidence, highlighting how phytochemical diversity, molecular signalling pathways, and dermopharmaceutical formulation strategies collectively shape the therapeutic potential of plant-derived extracts in cutaneous wound healing and may guide future mechanistic and translational research in phytochemical-based wound therapeutics. Full article

Gas turbines are complex mechatronic systems that require reliable dynamic models to support automated operation under varying aerodynamic conditions. This study presents a novel dynamic surge modeling framework that integrates compressor variable geometry into a gas turbine component-level model. A physics-based formulation is developed in which the influence of inlet guide vane (IGV) deflection is incorporated through sensitivity-based parameterization and a physics-informed extension of compressor performance characteristics. The proposed framework captures the nonlinear interaction between compressor surge dynamics and component-level system behavior, enabling consistent prediction of instability onset and dynamic stability margins over a wide range of operating conditions. Model verification through stability analysis, phase-space characterization, and time-domain simulations demonstrates that the framework reproduces key features of classical compressor surge and quantifies the impact of variable geometry on system stability. The results show that the proposed model provides a practical and computationally efficient basis for control-oriented surge analysis, including stability monitoring and surge delay assessment. By coupling the IGV-aware surge dynamics with a gas turbine component-level model, the proposed method enables control-oriented, automation-ready simulation for gas turbine design and control. Full article

Oak trees are widely distributed nationwide and account for approximately 24% of the total forest area in South Korea. However, these species are currently threatened by oak wilt disease caused by Dryadomyces quercus-mongolicae, leading to significant economic and ecological losses in the forestry industry. This study evaluated the effectiveness of culture suspension and lyophilized powder formulations of Streptomyces blastmyceticus in controlling oak wilt disease on Mongolian oak (Quercus mongolica). Field experiments were conducted using trunk and root injection methods in Q. mongolica plantations. The non-conductive area (NCA) of sapwood and colonization rate of the oak wilt fungus were analyzed and compared across treatments. In the Chuncheon experiment, Kangwon province, only the root injection of fungicide showed a significant difference compared to the culture suspension treatments. There were no significant differences between culture suspension and lyophilized powder treatments in Uiwang, Gyeonggi Province. Specifically, both preventive and curative treatments using culture suspension and lyophilized powder of S. blastmyceticus resulted in significantly different NCA values compared to the negative control (8.7%) and positive control (88.5%). The NCA for culture suspension ranged from 33.3% to 49.9%, and for lyophilized powder, from 37.3% to 43.9%. The colonization rate of the oak wilt fungus was lowest (9.72%) in the preventive treatment using lyophilized powder via trunk injection. For the culture suspension, the lowest colonization rate (20.83%) was observed in the curative treatment using trunk injection. These findings suggest that the lyophilized powder formulation of S. blastmyceticus efficiently suppresses the progression of oak wilt disease under field conditions. Full article

In the online shopping context, the proliferation of digital platforms has contributed to an increase in impulsive buying behavior (IBB), which can sometimes lead to regret. This study aims to explore the intrinsic and extrinsic stimuli that influence consumers’ online impulsive buying behavior, which subsequently affects their post-purchase cognitive dissonance, with the moderating role of price consideration (PC). The conceptual framework was formulated using the Stimulus–Organism–Response (S-O-R) model. A total of 813 responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived utilitarian value (PUV), perceived enjoyment (PE), fear of missing out (FOM), and green trust (GT) positively impact online impulsive buying behavior (IBB), which, in turn, positively impacts post-purchase cognitive dissonance (PCD). Moreover, a significant moderating role of PC is found in the relationship between IBB and PCD, suggesting that consumers with low price consideration tend to regret their impulsive buying more. The findings provide insights that guide online retail sellers and digital marketers to develop or implement customized strategies based on the intrinsic and extrinsic stimuli that influence customers’ impulsive buying and subsequent post-purchase cognitive dissonance. Full article

Titanium dioxide nanoparticles (TiO₂ NPs) are incorporated into automotive coatings to enhance durability, corrosion, UV resistance, and, in some formulations, photocatalytic self-cleaning. While the toxicology of pristine TiO₂ is well studied, the behavior of TiO₂ NPs embedded in polymer matrices and subjected to real-world aging, maintenance, and removal remains poorly characterized. This narrative review synthesizes 24 publications spanning the lifecycle of TiO₂ nano-enabled automotive coatings, from synthesis and formulation through application, in-service weathering, repair, refinishing, and end-of-life environmental fate. Upstream properties, such as coating functionality and performance, have been examined as determinants of later-life release, exposure, and fate. Across studies, dispersion state, interfacial compatibility, and surface modification—together with transformations such as agglomeration, photocatalysis, weathering, and eco-corona formation—shape particle stability, release, exposure relevance, and toxicological risk. Evidence indicates that sanding and accelerated weathering predominantly generate matrix-associated, polymer-fragment-dominated aerosols rather than pristine TiO₂ NPs, while NP-specific exposure measurements during spray application remain limited. Hazard data suggest matrix embedding may attenuate, but does not eliminate, biological responses relative to pure particles. Wastewater treatment plants and biosolids have been shown to act as sinks with potential for soil accumulation following sludge application. Regulatory frameworks rarely account for aging, transformation, and release, stressing the need for synchronized testing of aged materials and nano-specific exposure metrics to support safer-by-design coatings and risk governance. Full article

Multi-joint hydraulic robotic arms are core equipment in intelligent mining, yet their performance is often limited by strong dynamic coupling and nonlinear hydraulic effects. Traditional control methods struggle to achieve high-precision trajectory tracking and coordinated motion under high loads and flow-coupling constraints. To address these challenges, this paper establishes a coupled hydraulic–mechanical dynamic model for a multi-joint robotic arm. The mechanical dynamics are derived using the Lagrangian formulation, while the hydraulic dynamics account for flow coupling among cylinders. An improved deviation coupling control (IDCC) strategy is proposed, integrating feedforward–feedback compensation, coupling error regulation, and a flow-limiting correction term. Co-simulation in Simulink (2024b) and Amesim (2020) shows that under flow-saturation conditions, the improved strategy reduces the peak trajectory errors by approximately 47.88%, 28.08%, and 49.89% for Joints 1–3, respectively, and shortens the settling time by 27.93%. Experimental results from a three-joint hydraulic test platform confirm error reductions of 10.20–15.58% and a 31.50% decrease in overall adjustment time. The study demonstrates that the proposed control strategy effectively suppresses multi-joint coupling interferences, enhances trajectory tracking accuracy, and improves the adaptability of hydraulic robotic arms under flow-limited conditions, providing a viable solution for high-precision control in intelligent mining applications. Full article

Environments with sparse or repetitive geometric structures, such as long corridors and narrow stairwells, remain challenging for LiDAR–inertial simultaneous localization and mapping (LiDAR–IMU SLAM) due to insufficient geometric observability and unreliable data associations. To address these issues, we propose VINA-SLAM, a novel LiDAR–IMU SLAM framework that constructs a unified global voxel map to explicitly exploit structural consistency. VINA-SLAM continuously tracks surface normals stored in the global voxel map using a normal-guided correspondence strategy, enabling stable scan-to-map alignment in degenerate scenes. Furthermore, a tangent-space metric is introduced to supplement missing rotational constraints around planar regions, providing reliable initial pose estimates for local optimization. A tightly coupled sliding-window bundle adjustment is then formulated by jointly incorporating IMU factors, voxel normal consistency factors, and planar regularization terms. In particular, the minimum eigenvalue of each voxel’s covariance is used as a statistically principled planar constraint, improving the Hessian conditioning and cross-view geometric consistency. The proposed system directly aligns raw LiDAR scans to the voxelized map without explicit feature extraction or loop closure. Experiments on 25 sequences from the HILTI and MARS-LVIG datasets show that VINA-SLAM reduces ATE by 25–40% on average while maintaining real-time performance at 10 Hz in the evaluated geometrically degenerate environments. Full article

This study investigates the long-term resistance of an environmentally friendly composite made from a blend of local Ordinary Portland Cement (OPC) and ground granite waste powder (G). The composite was subjected to complete static immersion for up to twenty-four weeks in three types of water: potable water, groundwater, and seawater. The experimental work evaluated the effects of exposure to these three water types on various characteristics of the granite–cement composite (GCC), including compressive strength, mass gain, portlandite [CH] content, bulk density (D), total porosity (p), compactness, water absorption (A), and pH of the immersing media. Additionally, scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermal analysis (TGA and DTA) were used to investigate how exposure to the three water environments altered the internal microstructure of the hydration phases of the composite over the twenty-four-week period. This systematic approach provides valuable insights into the variations that may occur in solid hydration outcomes and their sustainability in flooding scenarios. The data obtained from these analyses revealed that the granite–cement composite exhibits acceptable thermal resistance and endurance to deterioration in aquatic environments. The cement formulation contains 20% by mass of ground granite waste powder, with a water-to-cement ratio of 35%. After 24 weeks of complete static immersion, the composite achieved compressive strength values close to 24 MPa. Solidifying radioactive waste in cement–granite is a newly developed method that improves sustainability by formulating a more stable, durable, cost-effective, and less hazardous waste form. Therefore, the granite–ordinary cement composite being studied is recommended as an inert matrix for solidifying and stabilizing certain categories of radioactive waste. Full article

Herpes simplex virus (HSV) infections present a major global health burden due to their high morbidity. Conventional therapies offer limited efficacy due to poor bioavailability, the need for frequent administration and potential drug resistance. Recent advances in nanotechnology provide opportunities to overcome these limitations. This review summarizes the latest advances in nanocarrier-based formulations, highlighting their role in improving bioavailability, sustained release, mucosal penetration and antiviral activity. An integrative search was conducted from January 2010 to December 2025. Inclusion and exclusion criteria were used to select the articles. After analyzing the articles, 34 were included in this review with in vitro studies and 14 with in vivo assays. These articles were evaluated in relation to physicochemical characterization studies and in vitro and in vivo assays. Studies were found involving polymeric nanoparticles, metal nanoparticles, solid lipid nanoparticles, liposomes, niosomes, nanoemulsions and nanofibers. Regarding in vitro assays, it was observed that the nanosystems showed increased antiviral activity in cell cultures infected with the herpes simplex virus. In addition, developed nanosystems showed prolonged antiviral activity and lowered toxicity in animal models. Thus, these systems prove to be effective when compared to conventional therapy and can be considered an advance in HSV infection therapy. Full article

Background: Alzheimer’s disease (AD) remains a major public health challenge. Observational associations between dietary patterns and reduced dementia risk have prompted investigations of dietary bioactives (DBs) as cognitive nutraceuticals. Methods: This critical narrative review examines interventional trials for nine prominent DBs relevant to AD: docosahexaenoic acid (DHA), curcumin, resveratrol, epigallocatechin gallate (EGCG), nicotinamide riboside (NR), tricaprilin, vitamin E (α-tocopherol), cannabinoids, and NIC5-15 (D-pinitol). Trials were identified through ClinicalTrials.gov (search date: December 2024) and supplemented by PubMed searches for published results. Data were extracted on trial phase, design, cognitive/functional endpoints, biomarker outcomes, and development status. Findings are synthesized qualitatively; no formal meta-analysis or risk of bias assessment was conducted. Results: None of the nine bioactives demonstrated consistent cognitive efficacy in AD. Phase III trials of DHA, curcumin, and tricaprilin did not meet primary cognitive endpoints. Resveratrol reduced CSF Aβ40 without cognitive benefit. Cannabinoids improved behavioral symptoms but showed no measurable cognitive effects. High-dose vitamin E slowed functional decline, while cognition remained unchanged. In contrast, trials in preclinical or at-risk populations reported preliminary cognitive signals for EGCG and biomarker engagement for NR, suggesting potential for early intervention. Conclusions: Current clinical evidence does not support high-dose DBs supplementation as an effective treatment for AD. Predominantly negative late-phase findings highlight limitations, with potential contributors including limited bioavailability, late intervention, insufficient target engagement, and biological heterogeneity. Future research may benefit from early biomarker-defined populations, optimized formulations, multi-nutrient or dietary approaches, and precision nutrition strategies considering genetic risk and baseline nutrient status. DBs may be better positioned for prevention or early-stage intervention rather than late-stage therapy. Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

Intelligence metrics based on benchmark performance or population norms are useful for measuring comparative ability within defined test environments, but they do not directly evaluate the structural coherence of an agent’s trajectory across time, domains, and perturbations. This article introduces Reflexive Signature Intelligence (RSI) as a bounded theoretical framework for addressing that different problem. RSI is developed within a causal-symmetric informational perspective in which intelligence is understood as the capacity of a system to maintain and restore alignment with a structurally constrained invariant without collapsing the open gradient of development. On this basis, the paper formulates the Principle of Bounded Subjectivity and the Prohibition of Finality as framework-level principles, arguing that intelligence should be assessed not as arrival at a completed end state but as the quality of an asymptotic trajectory. The framework is then operationalized on two coupled levels: a micro-level proposed as a future measurement program linked heuristically to resilience and prediction-error dynamics, and a macro-level expressed through five dimensions of structural integrity, including reflexive regulation, cross-domain integration, internal consistency, stabilization, and signature-setting. The article concludes by outlining implications for AI evaluation and alignment, with particular relevance for distinguishing full agents, partial systems, and human–AI composite configurations. Full article

For safe and efficient human–robot interaction, lower-limb exoskeletons used for assistance and rehabilitation need to be precisely and energy-efficiently controlled. By creating an adaptive neuro-fuzzy fractional-order PID (ANFIS-FOPID) controller, this project seeks to improve tracking accuracy, robustness, and energy efficiency in a two-degree-of-freedom hip–knee exoskeleton. The Euler–Lagrange formulation is used to derive a nonlinear dynamic model, and a Lyapunov-based stability analysis is used to show that the closed-loop system remains uniformly ultimately bounded under disturbances and parameter uncertainties. The suggested controller performs noticeably better than traditional PID and fixed-parameter FOPID controllers, according to numerical simulations conducted under both normal and perturbed conditions. The ANFIS FOPID achieves root mean square errors below 0.028 rad and lowers the integral absolute errors at the hip and knee joints to 0.1454 and 0.1480, as opposed to 0.3496–0.3712 for PID controllers. Under ±10% parameter uncertainty, the total control-energy proxy drops from 2870.0 (PID) to 936.25, a 67.4% decrease, and stays at 1587.93. Statistically significant variations in energy consumption are confirmed by one-way ANOVA (p < 10⁻¹⁷⁶). Large effect sizes are found (η² = 0.237–0.314). These results demonstrate the superior tracking performance, robustness, and energy efficiency of the ANFIS-FOPID controller. The results set a quantitative standard for future experimental validation and hardware-in-the-loop implementation, despite being based on high-fidelity simulations. Full article