Search Results (182,533)

Given that freeze–thaw damage of prestressed concrete significantly threatens structural service life and that existing conventional simulation techniques fail to capture prestress time series, this paper proposes a deep learning prediction model based on the Transformer model. The model integrates a multi-head self-attention mechanism and positional encoding to effectively capture long-range dependencies in prestressed time series. It enhances temporal modeling capability through a 128-dimensional high-dimensional feature space (chosen to balance representation capacity and computational efficiency for the dataset scale) and a 4-layer encoder stacking structure. A dataset was constructed using time-series data from three prestressed concrete components subjected to 50 freeze–thaw cycles. The F-a component was used as the training set, while F-b and F-c served as the testing sets. During the training phase, a Noam learning rate scheduler, gradient clipping, and an early stopping strategy were employed. The results indicate that the training strategy enables the loss function to converge quickly without overfitting, demonstrating good generalization performance. The prediction model performs well on the F-a and F-c datasets, with determination coefficients (R²) of 0.8404 and 0.8425, and corresponding Mean Absolute Error (MAE) of 61.71 MPa and 57.41 MPa, respectively. It can accurately track the periodic variation trend of prestress, demonstrating the model’s effectiveness in prestress prediction. This model provides a new technical tool for the health monitoring and performance prediction of prestressed concrete structures in freeze–thaw environments. Full article

This study aimed to design a multi-epitope vaccine (MEV) against Pasteurella multocida (Pm) using immunoinformatics approaches. Based on four conserved outer membrane proteins (OmpA; OmpH; PlpEand LolA), 15 immunodominant epitopes were identified, including 8 CTL epitopes, 3 HTL epitopes, and 4 B-cell epitopes. A vaccine construct was developed by incorporating RGD and PADRE adjuvant sequences. Computational analyses indicated that the vaccine possesses favorable physicochemical properties and structural stability. The molecular docking and normal mode analyses reveal a potential binding interface between the basis and TLR2/TLR4, with a computed binding energy of −10.1 kcal/mol for TLR4, suggesting a possible preferential interaction. Immune simulation predicted the vaccine could effectively elicit responses from B cells, T cells, and key cytokines such as IFN-γ. Additionally, the vaccine sequence was successfully cloned into the pET-28a (+) expression vector, facilitating future recombinant expression. This study provides a theoretical foundation for developing a safe and effective subunit vaccine against Pm. Full article

Enhancer of Zeste Homolog 2 (EZH2), the catalytic subunit of Polycomb Repressive Complex 2 (PRC2), mediates histone H3 lysine 27 trimethylation (H3K27me3), an epigenetic modification associated with transcriptional repression. EZH2 inhibitors (EZH2is) gained attention after the first-in-class drug Tazemetostat received FDA approval for treating epithelioid sarcoma. Preclinical studies suggest that EZH2is could be effective against melanoma, but their general inability to cross the blood–brain barrier (BBB), among others, limits the treatment of secondary brain metastases. Based on these limitations, we designed SG-8, a novel compound derived from TDI-6118 (a known brain-penetrant EZH2i). In silico docking predicted that SG-8 may exhibit high affinity for EZH2 as well as for another PRC2 subunit, Embryonic Ectoderm Development (EED). In addition, in vitro PAMPA assays suggested passive BBB permeability of SG-8. In cell-based assays, SG-8 and the structurally related EZH2i PF-06726304 displayed lower cytotoxicity than Tazemetostat in both primary (A375) and metastatic (Colo-679) human melanoma cells. Western blot analysis showed that SG-8 and PF-06726304 markedly reduced EED protein levels and, to a lesser extent, EZH2 levels, without affecting total H3K27me3, consistent with preserved canonical PRC2 activity. Instead, treatment with both compounds—most prominently SG-8—was associated with reduced phosphorylation levels of EZH2 (Ser21) and its upstream regulator Akt (Ser473), suggesting that modulation of the Akt–EZH2 signaling axis may at least partially contribute to their anti-melanoma activity. Full article

Nonlinear structural analysis serves as a fundamental tool for accurately predicting structural bearing capacity and ultimate strength. The incremental-iterative solution scheme represents the prevailing methodology for tracing nonlinear load–displacement responses and is implemented in most commercial finite element software. To enhance the robustness and computational efficiency of existing schemes, this paper first revisits the incremental-iterative framework, providing a detailed analysis that clarifies the distinct roles of the load increment factor in the predictor and corrector phases. Subsequently, a novel framework of updated orthogonal iterative schemes (UOIS) is established. Within this framework, the current generalized stiffness parameter (CGSP) and a cumulative indicator S_i are introduced in the predictor phase to adaptively control the magnitude and sign of the load increment, respectively. In the corrector phase, four enhanced orthogonal iteration strategies are formulated. Furthermore, to improve computational efficiency, a novel acceleration strategy is proposed, which embeds a secant prediction operator in the predictor phase, thereby circumventing the costly assembly and inversion of the tangent stiffness matrix. The results demonstrate that: (1) compared to the conventional generalized stiffness parameter (GSP), the proposed CGSP exhibits superior stability in tracking stiffness variations, offering a more reliable indicator for adaptive step-size control; (2) the cumulative indicator S_i reliably identifies load limit points and accurately distinguishes between loading and unloading regimes; (3) the UOIS framework demonstrates strong convergence in tracing complex equilibrium paths with multiple critical points and exhibits significantly superior robustness under large increment sizes compared to the generalized displacement control method (GDCM); and (4) the secant-prediction acceleration strategy achieves substantial improvements in computational efficiency without compromising solution accuracy. Full article

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024) . The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article

Accurate remaining useful life (RUL) prediction is essential for the safe and cost-effective operation of safety-critical systems such as electronic components and engines. While data-driven machine learning approaches have demonstrated strong performance for RUL estimation, their effectiveness is limited by the lack of full run-to-failure data and by strict privacy and intellectual property constraints in industrial settings. Federated learning (FL) enables collaborative model training across multiple data owners without direct data sharing, but it does not, by itself, provide formal privacy guarantees and remains vulnerable to information leakage. This paper presents a privacy-preserving DP-enhanced FL setup for RUL prediction that combines federated learning with differential privacy (DP). We describe an end-to-end implementation based on the Opacus DP library, highlight practical challenges arising from the integration of DP into recurrent neural network architectures, and propose solutions to address them. Using two representative RUL datasets (CMAPSS and SiC MOSFET), we analyze the effect of DP noise on prediction performance and on the functional dependence between the predicted RUL and the already lived life feature. The results demonstrate that differential privacy can be integrated into federated RUL prediction with limited degradation in predictive performance, providing practical insights for deploying privacy-aware collaborative models in industrial environments. Full article

Energy dissipation in stepped weirs depends on the complex interaction between geometry, flow regime, and surface aeration. The research proposes a dimensionless empirical model (RE3T) to predict the overall energy dissipation in three-section stepped weirs with variable slopes. The formulation integrates dimensional analysis based on the Vaschy–Buckingham theorem, controlled physical experimentation, and three-dimensional numerical simulations using CFD employing the RANS–SST turbulence model implemented in ANSYS CFX. Eighteen numerical simulations were performed covering seven geometric configurations and four hydraulic inlet conditions, covering slug, transitional, and skimming flow regimes. The CFD model was previously validated by comparison with a physical scale model, obtaining a discrepancy of only 0.38% in relative energy dissipation. The validated dataset was then used to calibrate an empirical multiplicative correlation composed of eight dimensionless groups associated with sectional slopes, number of steps, overall geometric ratio, and upstream Froude number. The proposed model achieved a coefficient of determination R² = 0.81, with relative errors generally less than 1% and a maximum deviation of 2.34%. The statistical indicators (RMSE, MAE, and bias) confirm the absence of significant systematic trends within the defined domain of validity. The results show that the Froude number and the slopes of the sections are the variables with the greatest influence on overall dissipation. The RE3T formulation is a physically consistent and computationally efficient predictive tool for the design and analysis of stepped weirs with variable slopes, extending the scope of traditional correlations developed for uniform slopes. Full article

Emergent properties are properties of a whole that cannot be reduced to the properties of its parts. Properties of a system can be defined as relations between a particular input given to a system and its corresponding output. From this perspective, whole systems formed by coupling component systems have properties different from the properties of their components. Wholes tend to arise spontaneously through a process of self-organization, in which components randomly interact until they settle in a stable configuration that in general cannot be predicted from the properties of the components. This configuration constrains the relations between the components, thus defining emergent “laws” that downwardly cause the further behavior of the components. Thus, emergent wholes and their properties arise in a simple and natural manner. Full article

Physiological dysregulation arising from chronic stress is a key mechanism linking psychosocial factors to long-term health outcomes, yet early identification typically relies on invasive or resource-intensive measurements. This study evaluates whether high-dimensional psychometric survey data can support scalable, non-invasive screening for latent physiological dysregulation. Using longitudinal data from the Midlife in the United States (MIDUS) Waves 2 and 3, we develop a screening-oriented modeling framework that separates longitudinal risk estimation from deployable screening model construction. Physiological targets are defined across inflammatory, metabolic, and neuroendocrine domains using three canonical allostatic load formulations. A teacher–ranking–pruning–student pipeline combines stable feature ranking, parsimony-driven dimensionality reduction, and knowledge distillation. Predictor dimensionality is reduced by more than an order of magnitude without loss of screening performance. Distilled student models consistently outperform linear, tree-based, and direct neural baselines, achieving area under the receiver operating characteristic curve values up to approximately 0.78 and substantial precision–recall lift over baseline prevalence. Longitudinal information is exploited during model development but not required at inference, enabling deployment using psychometric data alone. These findings demonstrate the feasibility of non-invasive screening for latent physiological dysregulation and provide a generalizable framework for translating longitudinal cohort data into deployable population health tools. Full article

Background/Objectives: Systemic inflammatory markers are increasingly recognized as prognostic indicators in metastatic colorectal cancer (mCRC), demonstrating significant associations with survival outcomes. The aim of this study was to evaluate the prognostic value of the Aarhus composite biomarker score (ACBS) in patients with metastatic colorectal cancer and to introduce the modified ACBS as a laboratory-based prognostic tool in mCRC. Methods: The Aarhus Composite Biomarker Score was calculated using serum albumin, C-reactive protein (CRP), neutrophil count, lymphocyte count, and hemoglobin levels. The modified Aarhus Composite Biomarker Score-1 (mACBS-1) stratified patients into three prognostic groups: favorable, intermediate, and poor risk. The simplified modified Aarhus Composite Biomarker Score-2 (mACBS-2) categorized patients into two prognostic groups (low vs. high risk). Survival analyses were performed using the Kaplan–Meier method, and prognostic factors were evaluated using Cox regression analysis. Results: The median overall survival (OS) was 35 months (95% CI: 29.38–40.62). Stratification by mACBS-1 revealed median OS values of 47, 30, and 14 months for favorable-, intermediate-, and poor-risk groups, respectively (p = 0.002). Similarly, mACBS-2 distinguished two prognostic groups, with median OS of 47 months in the favorable-risk group and 30 months in the poor-risk group (p = 0.001). In multivariable analysis, ACBS remained an independent predictor of overall survival, with three abnormal biomarkers conferring a significantly increased mortality risk (HR 4.61, 95% CI 2.17–9.82, p < 0.001). Similarly, poor-risk classification by mACBS-1 (HR 3.36, 95% CI 1.58–7.12, p = 0.002) and mACBS-2 (HR 2.05, 95% CI 1.29–3.26, p = 0.002) was independently associated with worse survival. Conclusions: The ACBS and its modified versions (mACBS-1 and mACBS-2) are simple, laboratory-based prognostic tools with independent predictive value for survival in metastatic colorectal cancer. Its clinical use may support improved risk stratification and individualized patient management. Full article

Objective: The biocompatibility of adhesive systems is essential for the long-term success of restorative dental procedures due to their close proximity to dentin and pulpal tissues. This study aimed to evaluate the cytotoxic effects of adhesive systems with different pH values on L929 mouse fibroblast cells under in vitro conditions. Materials and Methods: Four commercially available adhesive systems with different pH values—All-Bond Universal, G-Premio Bond, Tokuyama Bond Force II, and Clearfil Universal Bond Quick—were evaluated. Cytotoxicity was assessed using the MTT assay at four different concentrations (0.1%, 0.01%, 0.001%, and 0.0001%) and three incubation periods (24, 48, and 72 h). Cell viability data were analyzed using two-way analysis of variance followed by Bonferroni post hoc tests. Cytotoxicity was interpreted according to ISO 10993-5 criteria. Results: All adhesive systems exhibited concentration-dependent cytotoxicity, with significant reductions in cell viability observed only at the highest concentration (0.1%). At lower concentrations, no cytotoxic effects were detected. Despite having the highest pH value, All-Bond Universal consistently demonstrated the lowest cell viability. In contrast, Tokuyama Bond Force II showed the most favorable cytocompatibility profile, with relatively higher cell viability values over time. Morphological analysis supported the quantitative findings, revealing pronounced cellular alterations at high concentrations and preserved fibroblastic morphology at lower concentrations. Conclusions: adhesive systems demonstrate cytotoxic effects in a concentration-dependent manner, and pH alone is insufficient to predict their biocompatibility. Monomer composition and formulation characteristics appear to play a more critical role in determining cytotoxic behavior. These findings emphasize the importance of appropriate adhesive handling and isolation techniques to minimize tissue exposure and enhance clinical safety. Full article

The energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

Urban geophysical exploration faces significant hurdles due to strong electromagnetic interference and limited operational space, which restrict the efficiency and depth of traditional Electrical Resistivity Tomography (ERT). To overcome these limitations, this paper presents a novel ERT measurement and control system based on the Frequency Division Multiplexing (FDM) principle. Unlike conventional time-domain methods, this instrument synchronously transmits three independent AC signals at distinct frequencies. The acquisition station utilizes Fast Fourier Transform (FFT) to isolate specific frequency responses, enabling the simultaneous retrieval of apparent resistivity data for three different electrode spacings from a single transmission. The system architecture integrates low-power STM32 microcontrollers with an Android-based control terminal via Bluetooth, Wi-Fi, and NB-IoT technologies. This wireless design supports real-time current monitoring and cloud-based data synchronization. Experimental results demonstrate that the FDM operating mode significantly enhances data acquisition efficiency and anti-interference capability through frequency-domain separation. Controlled indoor and preliminary field tests indicate that FDM mode substantially improves acquisition efficiency through concurrent multi-channel measurement while effectively resolving target signals from noise. This study demonstrates the system’s technical feasibility and provides a practical foundation for future geophysical detection in time-constrained urban environments. Full article

The increasing frequency of torrential rainfall due to global warming has resulted in a significant rise in urban flooding and river overflows. Rainwater pumping stations, typically located near rivers, serve as buffers between sewer systems and receiving water bodies, helping to mitigate flood risks. A primary challenge in operating these stations is optimizing pump performance to prevent flooding while minimizing energy consumption and costs. Various computational methods, including meta-heuristics and deep learning, have been proposed to tackle this optimization problem. However, most studies either overlook or inadequately address pump maintenance costs, which are essential for long-term operational efficiency. This gap stems from the lack of a comprehensive model that accurately captures the full spectrum of costs involved in pump operation. This paper introduces a cost estimation model that integrates both deterministic and probabilistic elements to enhance the energy-efficient operation of rainwater pumping stations. The model focuses on pumps with capacities of 100 m³/min and 170 m³/min, which are commonly used. It takes into account electricity consumption costs as well as maintenance costs arising from frequent on/off cycles and dry-run events. Predictions of failures due to these operational stresses are modeled using the Crow–AMSAA non-homogeneous Poisson process (NHPP) and Weibull distributions—probabilistic models widely used in mechanical failure analysis. To evaluate the proposed model, simulations were conducted using the Storm Water Management Model (SWMM), comparing a deep reinforcement learning-based control strategy with the current operational method at the Gasan Pumping Station in Seoul, South Korea. The pump operating costs associated with each method were calculated and analyzed using the proposed model, demonstrating its potential for ensuring cost-effective and reliable pump operation. Full article