Search Results (20,970)

Introduction: Olives have been used in traditional Mediterranean medicine for thousands of years to address the causes of inflammation, ageing and cognitive health. Traditional preparations of olive include olive oil and olive leaf extract, which are major components of diets that contribute to maintaining cognitive function and reducing neurodegenerative disease risk. Aims of the study: This systematic review aimed to synthesise experimental and limited human evidence on olive biophenols in neurodegenerative disease models, identify the most studied compounds, characterise their mechanisms of action, and evaluate key translational barriers. Materials and methods: Following PRISMA 2020 guidelines and registered with PROSPERO (CRD420251252252), primary studies investigating the effects of well-characterised olive biophenols in neurodegenerative relevant in vitro, in vivo, or human models were systematically reviewed. Each study was assessed for its design, experimental model, mechanistic outcomes and reported limitations. Risk of bias was evaluated using validated tools (SYRCLE/OHAT/ToxR) appropriate for preclinical and experimental study designs. Results: Among the 25 studies, 7 (28.0%) examined oleuropein or oleuropein aglycone, 10 (40.0%) focused on hydroxytyrosol or its derivatives, and 9 (36.0%) investigated oleocanthal. Most studies employed in vivo animal models (57.7%), predominantly transgenic mouse models of AD and toxin-induced PD models. Oleuropein-based studies reported inhibition of amyloid-β and α-synuclein aggregation with behavioural improvements. Hydroxytyrosol primarily exerted antioxidant and anti-inflammatory effects with modest cognitive benefits. Oleocanthal showed the most consistent anti-amyloid and anti-tau activity, including enhanced amyloid-β clearance across the blood–brain barrier. Most studies show a moderate risk of bias due to incomplete reporting, randomisation and blinding. Conclusions: Olive biophenols demonstrate consistent neuroprotective effects in preclinical models; however, translation to clinical application remains limited by pharmacokinetic constraints, methodological heterogeneity, and insufficient human evidence. Full article

The shift toward lightweight powertrain architectures necessitates a detailed characterization of polymer gears to verify their efficiency and durability. This study investigated the effectiveness of non-contact structured-light 3D scanning for evaluating the surface topography and dimensional tolerance quality of polymer gears produced via distinct manufacturing technologies. A structured-light 3D scanner was used to capture dense point clouds (exceeding 6 million points) of gears produced by three methods: conventional hobbing (POM-C), Material Extrusion (MEX) with carbon fiber reinforcement, and Selective Laser Sintering (SLS). The manufactured parts were compared against the nominal Computer Aided Design (CAD) models to evaluate their geometrical deviations in accordance with DIN 3961 and surface roughness parameters per ISO 25178. The experimental results revealed a consistent ranking of manufacturing quality. The conventionally hobbed POM-C gear exhibited superior precision, achieving DIN quality grades of Q9–Q10 and the smoothest surface finish (S_a = 5.0 µm). Among additive manufacturing techniques, SLS-printed PA 12 showed intermediate quality (Q11, S_a = 12 µm), whereas MEX-printed PPS-CF exhibited significant deviations (exceeding Q12) and the highest surface irregularity (S_a = 25 µm) due to stair-stepping effects. These findings indicate that while additive manufacturing offers geometric flexibility, conventional hobbing retains a decisive advantage in dimensional precision. The optical scanning methodology demonstrated here constitutes an efficient metrological framework for gear quality control, with potential applications extending to the quality assurance of additively manufactured adaptive fixtures and assembly tooling, including automotive assembly operations. Full article

Flotation circuits typically incorporate grinding stages, yet mathematical models for these processes often operate on different principles, leading to misalignment in circuit design. Building on a previously established grinding model for flotation performance, this research introduces significant advances to develop a more comprehensive and industrially relevant framework. The primary innovation is the integration of mechanical entrainment and gangue recovery into the kinetic model, distinguishing between species captured by true flotation and those carried to the surface despite being non-hydrophobic. We developed a robust set of grinding-mill equations based on first-order kinetics to describe the mass-fraction transformation of both true-flotation and entrainment species. To ensure practical applicability, a systematic experimental and modeling methodology for parameter adjustment is introduced, providing a clear sequence for identifying breakage rate constants and flotation kinetic parameters. The proposed strategy was validated using two distinct case studies: an expanded analysis of a copper sulfide ore (ore A) and a new case involving significant gangue entrainment (ore B). The results demonstrate that the model accurately predicts species kinetics, providing a high-fidelity, cost-effective tool to optimize mineral recovery and prevent economic losses from overgrinding in industrial processing plants. Full article

Humanity is facing an unprecedented socio-ecological and climate crisis resulting from human impact on the planet, which requires a profound transformation in how we inhabit and develop our territories. Regenerative development is emerging as a key approach to strengthening living systems and improving environmental health. In this context, United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserves are consolidating their role as strategic instruments that link biodiversity conservation with sustainable development through integrated and participatory land management models. Mexico stands out for its regional and global leadership in implementing these areas. Participatory governance, promoted by the Man and Biosphere (MAB) programme, encourages the active involvement of local communities. This article analyses the application of a regenerative and participatory design methodology in a Biosphere Reserve, evaluating both the process and the tools used. Beyond the fulfilment of sustainability objectives, it examines the lessons learned, results and scope from a regenerative perspective, providing critical reflections on its effectiveness as a strategy for the socio-ecological management of vulnerable territories. Full article

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy–Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin’s minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton–Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

Background/Objectives: Psilocybin has re-emerged as a promising intervention for neuropsychiatric disorders including major depressive disorder, treatment-resistant depression, anxiety associated with life-threatening illness, obsessive compulsive disorder, and substance use disorders. However, conventional randomized controlled trials (RCTs)—the current gold standard in evidence-based medicine—may not adequately capture the therapeutic complexity of psilocybin, which depends not only on pharmacological action but also on contextual, psychological, and interpersonal factors. This critical narrative review aimed to evaluate the adequacy of existing clinical research frameworks for assessing psilocybin’s therapeutic potential and to explore alternative methodologies that may better reflect real-world clinical conditions. Methods: Using the Web of Science Core Collection database, we identified and analysed the ten most cited clinical studies on psilocybin published between 2015 and 2025 inclusive. Additional literature was included through reference cross-checking, systematic reviews, meta-analyses, and interdisciplinary sources covering neurobiology, history, and real-world evidence (RWE). The review synthesizes clinical outcomes, methodological constraints, and epistemic considerations relevant to psychedelic-assisted therapy. Results: Evidence from highly cited trials demonstrates rapid and sustained antidepressant and anxiolytic effects of psilocybin, with notable benefits also observed in addiction treatment. However, significant methodological limitations were identified, including selection bias, challenges in placebo design and blinding, small sample sizes, and the underrepresentation of diverse populations. Psilocybin outcomes were strongly influenced by subjective experience and contextual factors such as set and setting. Emerging RWE studies revealed heterogeneous patterns of response and provided insights unattainable through RCTs alone. Conclusions: Psilocybin shows considerable therapeutic promise, but current RCT methodologies capture only part of its clinical effects. Comprehensive evaluation will require larger and more diverse clinical trials, long-term follow-up, standardized psychotherapeutic protocols, and the integration of RWE to reflect real-world practice. Psychedelic-assisted therapy should be conceptualized as a complex intervention that combines pharmacological and psychotherapeutic components. Full article

This study develops a multiregional forecasting framework for road traffic accidents in Ecuador, addressing a critical limitation in existing predictive approaches that rely predominantly on point error metrics without validating the statistical assumptions underlying forecast uncertainty. Although the analysis is conducted at the provincial level, the spatial dimension is used primarily for cross-regional comparison and risk classification rather than for explicit spatial interaction modeling. Using a dataset of 27,648 monthly observations covering all 24 provinces from 2014 to 2025, the study applies the Prophet model within a Design Science Research paradigm and a CRISP-DM implementation cycle. Separate provincial models are estimated with a 24-month forecasting horizon, and methodological rigor is ensured through systematic residual diagnostics using the Shapiro–Wilk test for normality and the Ljung–Box test for temporal independence. Empirical results indicate that the Prophet-based artifact outperforms a naïve seasonal benchmark in 70.8% of the provinces, demonstrating excellent predictive accuracy in structurally stable regions such as Tungurahua (MAPE = 10.9%). At the same time, the framework enables the identification of critical emerging risks in provinces such as Santo Domingo and Cotopaxi, where projected increases exceed 49% despite acceptable point forecasts. The findings confirm that point accuracy alone does not guarantee the validity of confidence intervals and that residual validation is essential for trustworthy uncertainty quantification. Overall, the proposed approach provides a robust foundation for a predictive surveillance system capable of supporting differentiated, evidence-based road safety policies in territorially heterogeneous contexts. Full article

The call to decolonize our teaching, research, and universities is gaining momentum, and change begins with our everyday actions. In this concept paper, I advance critical reflective praxis—grounded in critical race theory, decolonial thought, and Indigenous studies—as a heuristic for identifying and challenging colonialism, Eurocentrism, racism, and other biases and systems of power across the entire research process, and for moving beyond critique into praxis. I also advance research as a site of praxis, and I argue for reconceptualizing praxis as praxis-in-motion, and for diagnostically evaluating praxis rather than assuming it is inherently ethical. To exemplify the process of critical reflective praxis, I evaluate a travel-based study I conducted about urban health and sustainable development in northeast Thailand that utilized the Moving Worlds Framework (also known as the travelogue methodology), a critical and decolonial approach to research that positions travel as a dynamic condition of knowledge production. In this evaluation, critical reflective praxis is operationalized as a whole-of-process intervention, embedding critical analysis, reflexivity, accountability, and praxis throughout the research process, based on social justice perspectives. My analysis demonstrates how bias can infiltrate research planning, design, methods, representation, and publication, even within decolonial methodological approaches. Critical reflective praxis is proposed as an evaluative and diagnostic tool for evaluating research and praxis. Full article

Translating heterogeneous user requirements (URs) into robust engineering specifications for public-access products is a critical challenge, often impeded by information uncertainty and fragmented design processes. To address this, we propose an integrated decision-making framework underpinned by Rough Set Theory (RST) as a unified mathematical language for uncertainty management. The framework systematically guides customer-driven product development by integrating a series of RST-based methods: a Kano model analysis to screen URs, a novel rough-Shapley value model to determine their interdependent weights, a rough-QFD approach to translate them into weighted design requirements (DRs), and the rough-VIKOR method to select the optimal design alternative. A case study on public-access faucets validates the framework’s efficacy. The results demonstrate its capability to identify critical URs, derive robust DRs by systematically resolving technical attribute conflicts, and select a superior design solution that optimally balances hygiene, durability, and user experience. The application of the framework successfully identified Alternative ${\mathrm{A}}_1$ (Push-Activated Spout) as the optimal solution, demonstrating superior performance in proactive hygiene and core functionality. The results prove that maintaining data integrity through a unified RST pipeline effectively resolves early-stage design conflicts. This research contributes a rigorous, data-driven decision support system that enhances objectivity and information fidelity, providing a transparent and auditable methodology for designing human-centered public infrastructure. Full article

Glioblastoma (GBM) remains one of the most lethal cancers, and despite advancements in understanding its underlying molecular signature, effective therapeutics are still lacking. The multifaceted challenges of designing treatments for GBM are compounded by the inability to identify a definitive cell of origin, the understanding of which is crucial for developing impactful therapies and ultimately improving patient outcomes. High-resolution technologies, including single-cell and single-nucleus RNA sequencing, spatial transcriptomics, multi-omics, next generation glioma models, bioinformatics, and artificial intelligence are creating an important opportunity to comprehensively map the cellular origin of GBM and its evolutionary dynamics. Accumulating evidence support neural stem cells (NSCs) and oligodendrocyte precursor cells (OPCs) as primary candidates, providing critical insights into the ontogeny of GBM. This comprehensive review synthesizes current knowledge on the cellular origins of GBM and evaluates advanced methodologies, deepening our understanding of its development. Full article

To address the challenge of autonomous process adaptation in non-standard components with continuously varying groove angles, this study proposes an intelligent decision-making framework based on Response Surface Methodology (RSM) for oscillation welding. Instead of solely identifying a single optimal parameter set, RSM is employed as a knowledge-modeling tool to reveal adaptive relationships between groove geometry and key welding parameters. A Central Composite Design (CCD) is utilized to establish predictive models for weld geometry under varying conditions: wire feed rate (8–12 m/min), travel speed (5–9 mm/s), travel angle (70–110°), oscillation amplitude (2–6 mm), dwell time (0.2–0.6 s), and groove angle (80–100°). The significance and adequacy of the models are validated through analysis of variance (ANOVA), demonstrating high predictive accuracy with all coefficients of determination (R²) exceeding 0.82. Furthermore, defect-aware physical constraints derived from the formation mechanism of bottom humping are incorporated into the optimization process, specifically restricting the travel angle to a push angle of 70–85° to ensure feasible and reliable decision outputs. Based on the established response surfaces, geometry-dependent parameter selection rules are derived to simultaneously optimize root penetration (target 8.5–10.5 mm) and sidewall fusion (>2.5 mm) for groove angles ranging from 80° to 100°. Experimental validation confirms that the proposed decision-making strategy achieves stable bead formation and defect-free fusion, demonstrating high quantitative reliability with root penetration prediction errors below 7% and bead width errors below 13%. This work bridges the gap between geometric perception and process control, providing a practical pathway toward intelligent and adaptive robotic welding of non-standard components. Full article

The increasing availability of multi-material fused filament fabrication (FFF) systems has intensified the need for a systematic understanding of interfacial adhesion between model and support polymers. In this study, the adhesion behavior of commonly used engineering thermoplastics and dedicated support materials was investigated in the context of multimaterial FFF. A comprehensive experimental methodology was developed, including a custom tensile test specimen and fixture specifically designed to quantify interfacial adhesion under controlled conditions. Material combinations based on ABS, ASA, PETG, and carbon-fiber-reinforced PA (PAHT-CF), together with manufacturer-recommended and alternative support materials, were evaluated using uniaxial tensile testing and fracture surface analysis. The results demonstrate that interfacial adhesion strongly depends on material compatibility and processing conditions, and that dedicated support materials generally provide lower adhesion than model–model combinations. However, significant deviations were observed: SUPP PA exhibited unexpectedly high adhesion when paired with PAHT-CF, while SUPP ABS proved to be a more versatile support across multiple model materials, offering a favorable balance between sufficient adhesion during printing and ease of removal. Several material pairs showed negligible adhesion, leading to separation during manufacturing and limiting their practical applicability. Microscopic analysis revealed the coexistence of diffusion-driven bonding, mechanical interlocking, and weak boundary layer effects. The findings highlight that optimal support performance requires neither minimal nor excessive adhesion, and provide experimentally validated guidance for selecting material combinations and process windows in multimaterial FFF. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

Abusive news refers to digital content designed to maximize clicks and advertising revenue through sensational headlines, repetitive postings, or emotionally charged language, rather than upholding journalistic integrity. Despite growing concerns about its impact on media credibility and public trust, existing detection approaches lack systematic categorization and type-specific methodologies. This study addresses this gap by proposing a six-type typology of abusive news—content recycling, keyword insertion, title–body inconsistency, commercial promotion, emotionally stimulating headline, and automatically generated types—based on five analytical dimensions: content structure, authenticity, algorithmic manipulability, sensationalism, and information-ecosystem impact. We developed type-specific detection pipelines combining BERT-based embeddings, TF-IDF features, and rule-based indicators and evaluated them using a large-scale Korean clickbait corpus. Results demonstrate that BERT achieves higher F1-scores (0.89) for automatically generated content, while TF-IDF with SVM provides more stable precision (0.60) for emotionally charged articles under class imbalance. Cross-domain experiments confirm that models trained on diverse, balanced topic sets generalize better than volume-focused models, with diversity improving F1-scores by up to 0.07. BERT models show higher false positive rates on repetitive legitimate content compared to TF-IDF approaches, highlighting the importance of type-adaptive architectures and diversity-aware data design in abusive news detection systems. Full article