Search Results (3,041)

Thin-walled components used in aerospace manufacturing are highly susceptible to machining-induced deformation due to their low structural stiffness and dynamic cutting instability. Although signal-based modeling approaches have been reported for machining process monitoring and performance evaluation, deformation prediction of thin-walled structures requires explicit consideration of structural flexibility. To address this challenge, a deformation error prediction framework integrating multi-source dynamic machining signals with static structural flexibility characteristics is proposed, enabling simultaneous representation of process dynamics and structural response. Kernel principal component analysis (KPCA) is employed to reduce the feature dimensionality, and the extracted low-dimensional features are subsequently used as inputs for a kernel-based support vector regression (KSVR) model to establish the prediction framework. The proposed method was validated through 25 milling experiments conducted on Al7075-T6 thin-walled workpieces, where deformation error was measured at predefined monitoring points under varying process conditions. The results indicate that the proposed model achieves high predictive accuracy for machining-induced deformation, with RMSE values below 13 μm and R² exceeding 0.89 on both validation and testing datasets, demonstrating strong agreement between predicted and experimental results. In addition, machining vibration amplitude exhibits a consistent correlation with deformation error, confirming that increased energy input and cutting instability significantly exacerbate thin-walled workpiece deformation. 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

In this work, we synthesized blue-fluorescent nitrogen-doped carbon quantum dots (N-CQDs) via a facile, economical, and environmentally friendly one-pot synthesis, using citric acid as the carbon source and polyethyleneimine (PEI) as the nitrogen dopant. The as-prepared N-CQDs exhibited uniform size distribution, with an average diameter of approximately 3 nm and a quantum yield of up to 23.6%. Based on the mechanism of HClO-triggered static fluorescence quenching and oxidation of surface amine groups on the N-CQDs, we established a quantitative detection platform for hypochlorous acid (HClO). The proposed method demonstrated a linear response over the concentration range of 0–40 μmol/L, with a detection limit as low as 0.17 μmol/L. It also featured a rapid response time (within 2 min), high selectivity, and strong anti-interference capability against various common species, including Cl⁻, H₂O₂, NO₂⁻, NO₃⁻, TBHP, TBO•, Br⁻, I⁻, S²⁻, F⁻, O²⁻ and HO•. Furthermore, the probe was successfully applied to detect HClO in real-world samples such as river water and beer. Owing to its outstanding photostability and low toxicity, it proved highly effective for monitoring intracellular HClO in living cells. Full article

Background: Pometia pinnata (matoa) peel powder attenuates high-fat diet-induced adiposity and hepatic lipid accumulation in rats, but the responsible compounds remain unclear. This study aimed to identify the bioactive compounds that may contribute to this phenotype, with an emphasis on pancreatic lipase inhibition as a candidate mechanism. Methods: Rats received high-fat diets containing matoa peel powder, or its water- or ethanol extraction residue. Visceral fat accumulation, hepatic lipid deposition, and serum lipid profiles were evaluated. An ethanol extract was fractionated by activity-guided column chromatography based on pancreatic lipase-inhibitory activity, and structures were identified by nuclear magnetic resonance analysis. Static in vitro gastrointestinal digestion was performed to assess inhibition of fatty acid release by the extract or isolated compounds. Results: The visceral adiposity- and hepatic lipid-modulating effects observed with matoa peel powder were retained in the water extraction residue but not in the ethanol extraction residue, suggesting removal of bioactive constituents by ethanol extraction. The ethanol extract inhibited pancreatic lipase (IC₅₀ = 740 µg/mL). Two active compounds—hederagenin saponin and protocatechuic acid—were isolated, and both inhibited pancreatic lipase (IC₅₀ = 149 µmol/L and 404 µmol/L, respectively). Under simulated digestion in vitro, the ethanol extract and protocatechuic acid reduced free fatty acid release, whereas hederagenin saponin did not. Conclusions: Matoa peel powder contains ethanol-soluble constituents, including pancreatic lipase-inhibitory compounds that may contribute to the modulation of adiposity and hepatic lipid metabolism in high-fat-diet-fed rats. The attenuation of individual-compound activity under simulated digestion is consistent with matrix- and intestinal milieu-dependent effects, and supports a multi-component mechanism involving saponins, phenolics (protocatechuic acid), and their intestinal biotransformation products. Full article

The increasing adoption of data-driven techniques in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities within the cybersecurity ecosystem; however, cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. Of primary concern is that the “sheriff-style” cybersecurity audit approach often fails to accurately portray the true state of an organization’s cybersecurity posture, often providing a misleading sense of assurance based solely on formal compliance and controls existence. This study proposes an Anti-Sheriff Cybersecurity Audit Model, that moves beyond cybersecurity control checklists, by integrating intelligence-informed risk assessments with structured human judgment to support a more robust, adaptive, and risk-oriented auditing process. Grounded in design science research (DSR), the proposed approach combines conventional binary compliance verification with intelligence-derived risk indicators and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance validation. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional cybersecurity audits. The findings indicate that combining intelligence-informed analytics with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed approach, therefore, provides a foundation for evolving cybersecurity auditing from just periodic compliance assessments, toward a continuous, risk-informed, and governance-aligned assurance system. Full article

Epigenetic dysregulation is a central driver of cancer progression, therapeutic resistance, and phenotypic plasticity. Among epigenetic mechanisms, microRNAs (miRNAs) and DNA methyltransferases (DNMTs) engage in reciprocal regulatory interactions that extend beyond transient gene control. Emerging evidence indicates that DNMT–miRNA feedback loops function as epigenetic memory units, stabilizing malignant cell states and enabling durable phenotypic inheritance even after removal of initiating stimuli under conditions shaped by persistent redox and stress signaling cues. In this review, we synthesize mechanistic, computational, and translational studies demonstrating how double-negative DNMT–miRNA feedback architectures generate bistable regulatory circuits that lock cancer cells into epithelial–mesenchymal transition, stem-like, and therapy-resistant states through redox-sensitive regulatory thresholds rather than static epigenetic alterations. This framework provides a unifying explanation for why transient environmental or therapeutic cues can induce long-lasting epigenetic reprogramming and why conventional single-target epigenetic inhibitors often fail to achieve durable clinical responses. Building on this concept, we propose that herbal medicines and plant-derived phytochemicals act as epigenetic reset signals capable of destabilizing pathological epigenetic attractor states encoded by DNMT–miRNA memory circuits by modulating intracellular redox balance and redox-responsive signaling pathways. Owing to their multi-component and systems-level regulatory properties, herbal interventions modulate miRNA expression, DNMT activity, and upstream stress-responsive pathways in a coordinated manner, facilitating transitions from memory-dominated states toward renewed epigenetic plasticity. We further discuss the translational implications of combining miRNA-based therapies with herbal medicine as a strategy for epigenetic reprogramming rather than transient suppression within a redox-guided therapeutic framework. Finally, we address key challenges and clinical feasibility considerations, including delivery, heterogeneity, and safety, and outline future directions for biomarker-guided and systems-informed epigenetic therapies that incorporate redox state as a functional determinant of epigenetic responsiveness. By reframing DNMT–miRNA interactions through the lens of epigenetic memory, this review highlights miRNA–herbal combination strategies as a forward-looking approach for overcoming therapeutic resistance and achieving durable reprogramming in cancer through selective manipulation of redox-sensitive epigenetic signaling circuits. Full article

Dynamic characterization of DC–DC power supplies typically requires dedicated dynamic voltage sources and electronic loads, which are often expensive and not readily available in low-cost laboratory environments. This creates a need for simple and flexible solutions capable of performing reliable line and load transient tests without complex auxiliary hardware. This paper presents a cost-effective technique for the dynamic testing of DC–DC power supplies, which can be applied with high versatility to both line and load transient testing. It is shown that injecting a perturbation signal into the feedback loop of a standard DC–DC regulator enables the regulator to operate either as a dynamic voltage source or as a dynamic electronic load, thus supporting both transient and small-signal AC characterization of a power supply under test. Analytical guidelines are provided to determine the static operating conditions and the achievable bandwidth of regulators operating in Dynamic Source Mode (DSM) and Dynamic Load Mode (DLM). The impact of voltage-mode and current-mode control strategies, as well as different error amplifier implementations, is investigated. Experimental line and load transient tests are carried out on interconnected switching and linear power supplies using Texas Instruments PMLK Series BUCK, BOOST, and LDO boards operating from 3.6 W to 36 W, with crossover frequencies up to 20 kHz. Measured injection gains and transient responses confirm the analytical predictions and demonstrate that FIT provides a simple, reliable, and cost-effective solution for dynamic testing of low-power DC–DC converters. Full article

Satellite glial cells (SGCs) in sensory ganglia are increasingly recognized as active regulators of neuronal excitability and inflammatory signaling involved in pain conditions. In craniofacial and orofacial pain, trigeminal SGCs exhibit stimulus-dependent responses that develop over time and contribute to disease-related plasticity. Additionally, advances in experimental modeling, computational analysis, and data integration have fueled interest in “digital twins” as tools for hypothesis generation and decision support in biomedicine. However, most current biomedical applications are loosely defined and rarely explicitly address glial biology. Here, we propose a digital twin-inspired framework focused on trigeminal satellite glial cells to combine stimulus-response experiments with computational state modeling. Instead of claiming a fully developed digital twin, we describe a hybrid experimental–computational approach where glial activation states are inferred from measurable outputs, iteratively refined, and used to explore what-if scenarios related to pain mechanisms and treatments. These scenarios are intended to guide experimental design and hypothesis prioritization rather than to generate clinical predictions. We detail how this framework could enhance understanding of underlying mechanisms, prioritize potential interventions, and align with New Approach Methodologies (NAMs) and the 3Rs by reducing exploratory animal use. We also discuss key limitations, including biological simplification, uncertainty, and translational challenges. By viewing glial systems as dynamic, updateable entities rather than static readouts, this approach offers a practical and ethically grounded pathway toward more integrated research on craniofacial pain. Full article

Living systems exhibit anticipation, adaptability, and resilience that cannot be fully explained by stimulus–response models, static homeostasis, or convergence-based optimization. This work addresses this gap by proposing a theoretical framework in which a central aspect of biological function is understood through the geometry and stability of distributions over unrealized but accessible future trajectories. We formalize these distributions as a counterfactual manifold, defined as a probabilistically supported subset of path space induced by a system’s effective internal dynamics. Using tools from information geometry and dynamical systems theory, we analyze adaptive systems that modify the laws governing their own future trajectories and construct explicit dual-channel adversarial dynamics that couple processes expanding future possibilities with antagonistic processes enforcing feasibility constraints. We show that adaptive systems of this kind are generically unstable, tending toward either collapse of accessible futures or unbounded sensitivity to perturbation. Constructive adversarial dynamics are sufficient to stabilize counterfactual geometry without requiring convergence to a fixed point. A minimal adversarial model reveals three generic regimes: collapse, runaway sensitivity, and bounded non-convergent regulation. The framework yields operational, falsifiable predictions through measurable proxies based on response diversity, perturbation sensitivity, recovery geometry, and boundary residence, allowing these regimes to be discriminated using finite observations without reconstructing underlying state-space dynamics. Interpreting disease as instability of counterfactual geometry provides a unifying language for understanding rigidity, volatility, and context dependence across biological domains. Rather than replacing mechanistic models, the proposed framework offers a higher-level geometric and dynamical perspective in which such models can be embedded and compared, shifting attention from component-level dysfunction to the stability of biological futures and establishing a principled foundation for analyzing disease, intervention, and adaptability across scales. Full article

Psychedelic-assisted therapies (PATs) can produce rapid and sustained antidepressant effects, yet variability in response remains substantial. Identifying predictors and moderators is essential for optimising patient selection, preparation, and delivery. To map and synthesise the evidence on the predictors of antidepressant response to classic/serotonergic psychedelics administered with psychotherapeutic support in adults with depressive disorders, including treatment-resistant depression. Following PRISMA-ScR principles, we conducted a scoping review of major biomedical and psychology databases (PubMed (MEDLINE), Embase, PsycINFO, and Web of Science) and trial registries (searches September–October 2025), supplemented by reference-list screening. We included randomised trials, open-label studies, and naturalistic cohorts reporting associations between candidate predictors (baseline traits/clinical features, set/setting variables, acute in-session phenomenology, and biological measures) and validated depression outcomes. We charted study characteristics, analytic approaches (including moderation/mediation where available), and indicators of robustness (e.g., adjustment for overall intensity, preregistration, external validation). A total of 48 studies were included in the review. Across study designs, process-level features during the dosing session were the most consistent correlates of antidepressant improvement. Greater emotional breakthrough, mystical/unitive experiences, and ego dissolution-linked reappraisal/insight generally predicted larger and more durable symptom reductions, whereas anxiety-dominant or dysphoric states tended to attenuate benefit, often independent of overall subjective intensity. Set and setting—particularly a stronger therapeutic alliance and music experienced as resonant—predicted both the emergence of therapeutically salient acute experiences and downstream clinical gains. Baseline moderators showed smaller and mixed effects: PTSD comorbidity sometimes weakened trajectories; extensive prior psychedelic exposure was associated with smaller incremental gains; demographics were typically uninformative. Converging biological findings associated better outcomes with markers consistent with increased neural flexibility and plasticity (e.g., less segregated network dynamics; EEG indices), alongside peripheral changes implicating neurotrophic, inflammatory, and HPA axis pathways. Current evidence suggests that antidepressant response in PATs is driven less by static patient characteristics and more by what occurs during dosing and how the context shapes that experience. Optimising preparation, alliance, and music; facilitating emotional breakthrough and meaning making; and mitigating anxious dysregulation are actionable levers. Future trials should harmonise measures, pre-specify and validate moderators/mediators, intensively sample in-session experience and physiology, and report benefits and harms more consistently. Full article

This paper presents the structural health monitoring (SHM) system applied to a 9 m composite outer wing box (OWB) specifically designed for a brand-new regional aircraft to detect barely visible impact damage (BVID) based on structural response data. The approach relies on different technologies to offer multilevel diagnosis, including impact detection as well as disbonding identification, localization, and sizing. The use of different sensing techniques based on piezoelectric transducers and distributed fiber optic sensors deployed all over wing structures is explored. Different features are simultaneously extracted from the propagating waves and from light scattering, able to detect low-energy BVID impact. In addition, the combined use of static and dynamic interrogation allows the estimation of the delamination surface after impact with good accuracy. The final test results on the OWB provided effectiveness in detecting, localizing, and tracking impact damage in the composite structure, ensuring long-term reliability and safety, as well as characterizing barely visible damage by a fully integrated onboard SHM system. Full article

Continuous plantar-pressure monitoring is important for objective gait analysis and early detection of abnormal loading; however, many existing solutions remain laboratory-bound (force plates and instrumented walkways) or rely on costly in-shoe multilayer sensor arrays. Here, we developed and optimized a fully screen-printed pressure-sensing insole based on carbon–polymer nanocomposite layers, with an emphasis on manufacturability and process control to bridge the gap between proof-of-concept force-sensitive resistor (FSR)-based insoles and scalable printed-electronics manufacturing workflows. Composite pastes containing carbon fillers (graphene nanoplatelets, carbon black, and graphite) were formulated to improve sensor repeatability and sensitivity. Sensors were characterized under compression loads from 100 N to 1300 N, showing a sensitivity of 10.5 ± 2.8 Ω per 100 N and a sheet-to-sheet coefficient of variation of 22.1% in resistance response. The effects of paste composition, screen mesh density, electrode layout, and lamination on sensitivity and repeatability were systematically evaluated. In addition, correlation analysis of resistance values from integrated quality-control meanders proved useful for monitoring screen-printing process stability. The final insole integrates printed carbon sensing pads and contacts, a dielectric spacer, and an adhesive layer in a thin, flexible format suitable for integration with wearable electronics. In practical static-load tests, repeated manual placement of weights yielded coefficients of variation as low as 4% at 500 g and a detection limit of ~0.1 N, comparable to a very light finger touch. These results demonstrate that low-cost screen-printed electronics can provide robust pressure sensing for wearable plantar-pressure monitoring. Full article

Background: Stress shielding, which occurs when there is a mismatch between the stiffness of the implant and the bone, can alter load transfer and drive peri-implant bone remodeling, particularly in low-density bone. Methods: We compared the biomechanical responses of one-piece implants made of Ti-6Al-4V, Y-TZP, and CFR-PEEK. We modelled the bone as linearly isotropic in the transverse direction and the implants as linearly isotropic with a fully bonded interface. A static load of 200 N was applied at an inclination of 30° buccal-to-lingual, with the transverse bone bottom faces fully constrained. Results: The peak cortical von Mises stress was highest for Y-TZP (87 MPa), followed by Ti-6Al-4V (57 MPa) and CFR-PEEK (approximately 37 MPa). Peak cortical von Mises strain showed the same relative order of magnitude: 3450 µε, 3103 µε, and 1523 µε, respectively. The stress-shielding factor (SSF) revealed that shielding was present in the mid-apical regions. Y-TZP exhibited the greatest shielding (SSF: 0.844–0.877), followed by Ti-6Al-4V (SSF: 0.380–0.568) and CFR-PEEK (SSF: 0.375–0.437). No crestal shielding was observed (SSF < 0). Conclusions: Overall, implants with higher stiffness increased crestal stress concentration and deepened peri-implant shielding. Meanwhile, CFR-PEEK improved load sharing and produced a more homogeneous mechanical stimulus in low-density bone. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article

Intensive Care Unit (ICU) readmissions following Intracerebral Hemorrhage (ICH) are associated with increased mortality and resource burden. Current prediction models predominantly rely on static admission features, failing to capture the temporal evolution of physiological instability. This study proposes a novel deep learning framework to predict ICU readmission by leveraging high-resolution time-series data from the MIMIC-III and MIMIC-IV databases. We developed a Stacked Gated Recurrent Unit (GRU) Architecture Ensemble, integrated with Time-series Generative Adversarial Networks (TimeGAN) to address the inherent class imbalance of readmission events. Our model achieved a state-of-the-art Area Under the Receiver Operating Characteristic Curve (AUC) of 0.912, significantly outperforming traditional machine learning baselines and static feature models. The sensitivity of 88.1% highlights the model’s efficacy in minimizing unsafe premature discharges. Furthermore, interpretability analysis using SHAP values identified Length of Stay, MELD Score, and Monocytes as critical predictors, revealing that readmission risk is driven by a complex interplay between systemic organ dysfunction and inflammatory response. These findings demonstrate that incorporating temporal dynamics and generative data augmentation significantly enhances risk stratification, offering a robust clinical decision support tool to optimize discharge timing in neurocritical care. Full article