Search Results (5,620)

Advanced glycation end products (AGEs) are important biomarkers associated with diabetes and metabolic disorders; yet existing detection methods are invasive and unsuitable for frequent monitoring. This study aimed to develop a non-invasive and portable AGEs detection device, optimize strategies for mitigating pigmentation-related interference, and evaluate its feasibility for metabolic assessment. The proposed system employs a 365 nm ultraviolet LED excitation source, an optical filter assembly integrated into an ergonomic dark chamber, and an eyelid-signal-based algorithm to suppress ambient light and skin pigmentation interference. Simulation experiments were conducted to evaluate the influence of different pigment colors and skin tones on fluorescence measurements. A clinical study was performed in 200 participants, among whom 42 underwent concurrent serum AGEs measurement as the reference standard. Predictive models combining corneal fluorescence signals and body mass index (BMI) were constructed and evaluated. The results indicated that purple and blue pigments introduced greater interference, whereas green and pink pigments had minimal effects. Device-derived AGEs estimates demonstrated good agreement with serum AGEs, with a mean error below 8%. A hybrid model incorporating BMI achieved improved predictive accuracy compared with single-parameter models. Participants with high-AGE dietary habits exhibited elevated fluorescence signals and BMI. These findings suggest that the proposed device enables stable and accurate non-invasive AGEs assessment, with potential utility for metabolic monitoring. Incorporating lifestyle-related parameters may further enhance predictive performance and expand clinical applicability. Full article

Background: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and is associated with increased mortality, prolonged length of stay (LOS), and greater resource utilization. Widely used AF risk scores were developed for stable outpatient populations and have limited applicability in critically ill patients. This study aimed to (1) characterize ICU patients with AF, (2) develop and temporally externally validate machine learning models to predict ICU mortality and ICU LOS, and (3) identify early clinical factors associated with these outcomes using interpretable methods. Methods: Adult ICU patients with AF from MIMIC-IV (n = 20,058) were used for model development with grouped cross-validation, and MIMIC-III (n = 11,475) served as a temporal external validation cohort. Predictors included demographics, admission characteristics, vital signs, laboratory values, vasoactive support, and AF-related medications available within the first 24 h of ICU admission. Eight classification algorithms were evaluated for ICU mortality, and six regression algorithms were evaluated for ICU LOS. Discrimination was primarily assessed using the area under the receiver operating characteristic curve (AUC) and average precision (AP), with additional threshold-dependent metrics reported to characterize operating-point behavior under low event prevalence. Probability-threshold optimization using out-of-fold predictions was applied to the primary mortality model. LOS performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R²). Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The median age was 75 years, and ICU mortality was 8.9%. For mortality prediction, the XGBoost model demonstrated preserved discrimination on temporal external validation (MIMIC-III) (AUC = 0.743; AP = 0.226). At the default probability threshold (0.50), recall and F1 scores were low due to low event prevalence; applying a prespecified F1-optimized threshold derived from the development cohort improved sensitivity while maintaining overall discrimination. For ICU LOS, models explained little variance on temporal validation; LightGBM performed best, but the explained variance was low (MAE = 88.9 h; RMSE = 163.9 h; R² = 0.038), indicating that the first 24-h structured data provide an insufficient signal to accurately predict ICU LOS, likely due to downstream clinical and operational factors. SHAP analysis identified clinically plausible predictors of mortality and prolonged ICU stay, including reduced urine output, renal dysfunction, metabolic derangement, hypoxemia, early vasopressor use, advanced age, and admission pathways. Full article

Underwater optical wireless communication (UOWC) systems are significantly challenged by turbulence-induced signal degradation in dynamic channel conditions. This paper presents a novel framework that integrates Long Short-Term Memory (LSTM) networks with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enable real-time turbulence prediction and multi-objective adaptive optimization of transmission parameters, including power, modulation scheme, and beam divergence. Experimental results demonstrate that the proposed LSTM–NSGA-II framework achieves a 45% reduction in bit error rate (BER) and a 36% improvement in energy efficiency compared to conventional static systems, while maintaining a signal-to-noise ratio (SNR) prediction accuracy of 94.7% and an adaptive response latency of 28.6 ms. Validation using field data from the Marine Institute in the Baltic Sea confirms the framework’s practical applicability and robustness, highlighting its potential to enhance autonomous and military underwater operations in turbulent environments. This work represents a significant step toward more reliable and efficient UOWC systems. Full article

Due to the limited number of deep sound speed profile (SSP) samples, the existing wide-area SSP inversion methods cannot estimate the full-depth SSP. In this paper, the full-depth SSP inversion is achieved by adding physical mechanism constraints to the neural network inversion algorithm. A dimensionality reduction approach for SSP perturbation, based on the hydrodynamic mechanism of seawater, is proposed. Constrained by the characteristics of ocean stratification, a self-organizing map is employed to invert the depth of the sound channel axis and reconstruct the SSP from the sea surface to the sound channel axis. The SSP from the sound channel axis to the seabed is reconstructed by integrating the characteristics of the sound channel axis and the sound speed gradient characteristics of the deep sea isothermal layer. The efficacy of the method was validated by the Argo data from the South China Sea. The average root mean square error of the reconstructed full-depth SSP is 2.85 m/s. Additionally, the average error of transmission loss prediction within 50 km is 2.50 dB. The proposed method is capable of furnishing effective full-depth SSP information without the necessity of any in situ measurements, thereby meeting the requirements of certain underwater acoustic applications. Full article

Accurate forecasting of cooling loads is essential for the effective operation of Building Energy Management Systems (BEMSs) and the reduction of building-sector carbon emissions. Although Artificial Neural Networks (ANNs), particularly Multi-Layer Perceptrons (MLPs), have shown strong capability in modeling nonlinear thermal dynamics, their reliability in practice is often limited by inappropriate training algorithm selection and poor data quality, including missing values and numerical distortions. To address these limitations, this study conducts a comprehensive empirical investigation into the effects of training algorithms and systematic data preprocessing strategies on cooling load prediction performance using an MLP model. Through benchmarking ten distinct training algorithms under identical conditions, the Levenberg–Marquardt (LM) algorithm was identified as achieving the lowest prediction error when integrated data preprocessing was applied. In particular, the application of data preprocessing reduced the CvRMSE from 18.56% to 6.03% during the testing period. Furthermore, the proposed framework effectively mitigated zero-load prediction errors during non-cooling periods and improved prediction accuracy under high-load operating conditions. These results provide practical and quantitative guidance for developing robust data-driven forecasting models applicable to real-time building energy optimization. Full article

Direct methods for solving protein crystal structures from X-ray diffraction data provide an essential approach for validating predicted models while avoiding external model bias. Nevertheless, traditional iterative projection algorithms, including the widely used Difference Map (DiffMap), are often limited by modest phase retrieval success rates. To address this limitation, we introduce a novel Hybrid Difference Map (HDM) algorithm that synergistically combines the strengths of DiffMap and the Hybrid Input–Output (HIO) method through six distinct iterative update rules. HDM retains an optimized DiffMap-style relaxation term for fine-grained density modulation in protein regions while adopting HIO’s efficient negative feedback mechanism for enforcing the solvent flatness constraint. Using the transmembrane photosynthetic reaction center 2uxj as a test case, the first HDM formula, HDM-f1, successfully recovered an atomic-resolution structure directly from random phases under a conventional full-resolution phasing scheme, demonstrating the robust phasing capability of the approach. Systematic evaluation across 22 protein crystal structures (resolution 1.5–3.0 Å, solvent content ≥ 60%) revealed that all six HDM variants outperformed DiffMap, achieving 1.8–3.5× higher success rates (average 2.8×), performing on par with or exceeding HIO under a conventional phasing scheme. Further performance gains were achieved by integrating HDM with advanced strategies: resolution weighting and a genetic algorithm-based evolutionary scheme. The genetic evolution strategy boosted the success rate to nearly 100%, halved the median number of iterations required for convergence, and reduced the final phase error to approximately 35${}^{\circ }$ on average across test structures through averaging of multiple solutions. The resulting electron density maps were of high interpretability, enabling automated model building that produced structures with a backbone RMSD of less than 0.5 Å when compared to their PDB-deposited counterparts. Collectively, the HDM algorithm suite offers a robust, efficient, and adaptable framework for direct phasing, particularly for challenging cases where conventional methods struggle. Our implementation supports all space groups providing an accessible tool for the broader structural biology community. Full article

Distributed Interactive Simulation (DIS) systems are highly sensitive to temporal delays. Conventional Dead Reckoning (DR) algorithms suffer from limited prediction accuracy and are often inadequate in mitigating simulation latency. To address these issues, a heuristic hybrid prediction model based on Polar Lights Optimization (PLO) is proposed. First, the Transformer architecture is modified by removing the decoder attention layer, and its temporal constraints are optimized to adapt to the one-way dependency of DR time series prediction. Then, a hybrid model integrating the modified Transformer and LSTM is designed, where Transformer captures global motion dependencies, and LSTM models local temporal details. Finally, the PLO algorithm is introduced to optimize the hyperparameters, which enhance global search capability and avoid premature convergence in PSO/GA. Furthermore, a closed-loop mechanism integrating error feedback and parameter updating is established to enhance adaptability. Experimental results for complex aerial target maneuvering scenarios show that the proposed model achieves a trajectory prediction $R^2$ value exceeding 0.95, reduces the Mean Squared Error (MSE) by 42% compared with the results for the traditional Extended Kalman Filter (EKF) model, and decreases the state synchronization frequency among simulation nodes by 67%. This model significantly enhances the prediction accuracy of DR and minimizes simulation latency, providing a new technical solution for improving the temporal consistency of DIS. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

Routine chemical assays, which are more readily available than direct mineralogical analyses, offer a rapid and cost-efficient approach of estimating mineral grades for geometallurgical modelling. This paper addresses the prediction of ore minerology from chemical assays for lithium-bearing pegmatites by implementing and comparing two element-to-mineral conversion (EMC) approaches: (1) mass balance techniques using two calculation variants and (2) machine learning methods (MLM). Both routines of the mass balance approach achieved satisfactory R² values exceeding 0.8, although calculation routine 1 was unable to automatically differentiate between the two lithium-bearing phases (spodumene and cookeite). Of the eight algorithms applied for the MLM approach, the top three performing models achieved R² values greater than 0.6 for both training and testing datasets, with slightly lower error evaluation metrics compared to the mass balance approach. Based on data accuracy requirements across the Mine Value Chain, the mass balance approach is suitable for the feasibility and operational stages, while the MLM approach meets the minimum data accuracy requirements of the scoping and pre-feasibility stages. However, it should be noted that the mass balance approach is limited to deposits with simple mineral assemblages while the MLM approach can handle deposits with greater elemental overlap among minerals. Full article

The probability integral method is the primary technique for predicting mining-induced subsidence in China, and its predictive accuracy strongly depends on the precision of the model parameters. To improve the accuracy and stability of parameter inversion and to overcome the convergence randomness of the Genetic Algorithm (GA) in global search, as well as the tendency of the BFGS quasi-Newton method (BFGS) to converge to local optima in non-convex optimization problems, a hybrid GA–BFGS optimization algorithm is proposed for inverting the parameters of the probability integral model. This hybrid approach combines the global exploration capability of GA with the fast local refinement of BFGS, resulting in a more efficient and robust parameter optimization process. Simulation results under ideal conditions without model error demonstrate that the proposed GA–BFGS algorithm outperforms pattern search (PS), GA, and BFGS in terms of inversion accuracy, convergence stability, and robustness to noise and outliers. In engineering applications, the inversion accuracy is reduced compared with simulation experiments, which can be attributed to complex geological conditions and inherent model uncertainties. Therefore, further improvements in subsidence prediction accuracy require not only refined inversion algorithms but also the development of more accurate prediction models that explicitly account for site-specific geological and mining conditions. Full article

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article

Background: This study aimed to evaluate different Bayesian algorithms and the first-order pharmacokinetics (PK) equation approach for forecasting vancomycin concentrations in critically ill pediatric patients and to identify influencing factors. Methods: A cohort of 110 patients with 568 therapeutic drug monitoring (TDM) blood samples was included. Three Bayesian algorithms, i.e., conventional, flattened, and weighted-flattened, using one or two historical values of either blood concentrations measured at the peak, trough, or middle (mid) of the dosing interval, were applied to forecast the concentrations of the next TDM occasion. The first-order PK approach, according to the Sawchuk–Zaske method, was used with two levels. The forecasting performance was assessed via relative bias (rBias) and relative root mean squared error (rRMSE) between the forecasted and observed levels. A linearmixed-effects model was employed to identify potential influencing factors on the rBias and rRMSE. Results: All methods showed negative rBias values of less than −20% and had relatively similar rRMSE of about 40%. First-order PK had lower bias than the conventional and flattened Bayesian algorithm (−10% vs. −15%), but higher bias than the weighted-flattened Bayesian algorithm (rBias −5%). Multivariate analysis using the linear mixed-effects model revealed that the type of forecasting algorithms significantly impacted the predictability. The weighted-flattened Bayesian algorithm significantly improved the rBias by 12.660% (95% CI: 10.131–15.194, p-value < 0.001) and decreased the rRMSE by 2.099% (CI 95% 3.779–0.418, p-value = 0.014) compared to the conventional Bayesian model. Either using one (mid or trough) or two concentrations in Bayesian forecasting yielded comparable rBias and rRMSE. Conclusion: The weighted-flattened Bayesian estimation method with solely one blood level is appropriate for forecasting the vancomycin concentration during therapeutic drug monitoring in critically ill children. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

The predictive accuracy of the Bekker–Wong model for wheel traction is highly dependent on the precision of the wheel–soil contact angle parameters. These parameters are typically identified through extensive and costly single wheel–soil tests, which are limited by poor experimental repeatability and site-specific constraints. This study proposes a method for obtaining contact angle parameters through numerical simulation. Firstly, a finite element model of an off-road tire is established. The Drucker–Prager (D-P) constitutive model parameters of clay under different moisture were calibrated by soil mechanical tests. And then the moist clay was modeled through the SPH algorithm. An FEM–SPH interaction model was developed to define the tire–moist clay interaction. Meanwhile, the tire–moist clay interaction model was verified by a single wheel–soil test device. To identify the empirical parameters of tire–soil interaction, numerical simulations were conducted for multiple operating conditions involving different slip ratios, soil moisture contents, and vertical loads. By processing the simulated wheel–soil contact characteristic images, the contact angles for each condition were extracted. Finally, the contact angle parameters in the Bekker–Wong model were identified. The empirical parameters were integrated into the Bekker–Wong model to predict traction. The results indicate that the maximum relative error of traction force between the prediction and experiment did not exceed 13.6%, which validated the reliability of the proposed method. Full article