Search Results (1,832)

Published gaining-sharing knowledge (GSK) variants adapt scalar parameters and donor/selection policy to a single operating point. Dimension-Tiered GSK (DT-GSK) selects its configuration by dimension: an adaptive scaffold serves every tier, a deterministic, budget-exact final refinement runs once at $D\ge 50$, with the GSK vector-update equations retained; an exploratory interaction-structure memory supplies the refinement’s basis. Against six GSK-family baselines re-executed on five CEC suites under one budget-fair paired protocol, DT-GSK attains the best descriptive family-rank aggregate on CEC2017 (2.48) and CEC2013 (2.80), though Holm-corrected tests separate it from eGSK on either suite only at $D=10$; it is second behind eGSK at CEC2017 $D=30$ and on CEC2011 (Holm-significant loss). On AGSK’s strongest suite, the CEC2020 competition in which it was the runner-up, DT-GSK places fourth; the family panel corroborates AGSK’s published strength in this low-dimensional boundary regime, where every dimension-gated DT-GSK subsystem is inactive ($D\le 20$). On CEC2013LSGO the comparison is family-internal: tied-first descriptive rank; paired tests do not separate DT-GSK from AGSK. Suite roles: CEC2017 selection-exposed; CEC2011/CEC2013 corroborative; CEC2020 pre-registered confirmatory; CEC2013LSGO post hoc. Direct isolations find no standalone benefit from that memory; coordinate axes outperform its basis: controlled negative results. All findings are scoped to the GSK-family panel. Full article

Anaerobic digestion of agro-industrial residues supports waste valorization and renewable-energy production, but reliable prediction requires validation that accounts for repeated measurements within reactors. This study compared 16 regression models for predicting cumulative methane production from digestion time and banana peel–sugarcane molasses composition using 5007 observations from seven batch reactors. Models were evaluated by leave-one-reactor-out cross-validation (LORO-CV). Radial-basis-function support vector regression (SVR-RBF; C = 10, gamma = “scale”, epsilon = 0.1) achieved the lowest pooled RMSE (118.09 NmL CH₄), with R² = 0.9482 and MAE = 75.75 NmL CH₄, and was selected as the surrogate model. However, reactor-level Wilcoxon tests with Holm correction showed no significant differences between SVR-RBF and the other algorithms. Held-out-reactor R² values ranged from −1.366 to 0.928, indicating heterogeneous generalization. Differential Evolution consistently identified approximately 100% banana peel and 0% molasses as the optimal composition. Across 70 runs, the median optimum was 310.10 h and 1433.25 NmL CH₄. Bootstrap analysis placed 99% of composition optima at ≥99% banana peel, although uncertainty in optimal time was substantial. Kinetic benchmarking supported the slower, higher-volume methane production observed in complete banana-peel reactors. This boundary solution is therefore a model-supported candidate requiring experimental confirmation, not a universal co-digestion optimum. Full article

Wildfire in African savannas reflects coupled surface thermal, hydroclimatic, vegetation, and disturbance processes, but basin-scale time-series analyses can overstate mechanisms when temporal dependence and spatial heterogeneity are ignored. We assembled a monthly 2003–2024 multi-source environmental dataset for the Zambezi River Basin ($n=263$) and nine Level-4 HydroBASINS units, using quality-controlled MODIS burned area, normalized difference vegetation index (NDVI), daytime land surface temperature (LST), and evapotranspiration (ET), together with CHIRPS precipitation and GLDAS-2.1 Noah 0–10 cm soil moisture. Primary inference used heteroskedasticity- and autocorrelation-consistent regressions, expanding-window Random Forest validation, false-discovery-rate-controlled distributed-lag tests, and sub-basin robustness; vector autoregression was retained as a secondary diagnostic. Current-month burned-area anomalies were positively associated with LST ($\beta =0.340$, $p=0.0015$) and negatively associated with near-surface soil moisture ($\beta =-0.326$, $p=0.0005$). Over 1–9 months, precipitation and soil-moisture histories were jointly supported after multiplicity correction, whereas basin-wide NDVI, ET, and LST histories were not. Burned-area history was followed by cumulative 0–6 month declines in NDVI ($\beta =-0.280$, 95% CI $[-0.452,-0.107]$) and ET ($\beta =-0.214$, 95% CI $[-0.419,-0.008]$). Directions were broadly consistent across sub-basins, although magnitudes varied. The evidence supports time-scale-specific conditional associations without structural-causal claims. Full article

To address the amplification of correction errors caused by an ill-conditioned AC power-flow Jacobian under heavily loaded operating conditions, as well as the excessive circuit depth associated with a fixed quantum-solution accuracy, this paper proposes a residual-controlled, regularized quantum singular-value transformation (QSVT) method within an inexact Newton power-flow framework. First, the power-mismatch vector and state variables are scaled, and the Newton correction is reformulated as a Tikhonov-regularized least-squares subproblem. A bounded regularization filter is then approximated using Chebyshev polynomials, allowing QSVT to directly transform the singular values of the Jacobian matrix. On this basis, the residual of the linear subproblem associated with the quantum-approximate correction is defined, and constraints are established to relate the polynomial-approximation error, block-encoding error, and quantum measurement error to the inexact Newton forcing term. The QSVT polynomial degree and quantum-solution accuracy are subsequently adjusted dynamically according to the outer power-flow residual. Theoretical analysis establishes the boundedness of the regularized correction, a sufficient descent condition for the quantum-approximate correction, and the relationship between the solution error and cumulative query complexity under dynamically controlled quantum accuracy. Full article

Existing DNAPL groundwater source inversion approaches are confronted with prominent bottlenecks: shallow surrogate models often fail to capture strong nonlinear multiphase flow relationships, traditional heuristic optimizers suffer from premature convergence, and ill-posed equifinality further degrades inversion reliability, together with prohibitive computational costs from repeated multiphase numerical simulation. Taking a typical chemical-contaminated site in Northeast China as the research object, this study establishes a multiphase flow numerical model that fully reproduces the migration and transformation mechanisms of chlorobenzene-based DNAPLs after systematic generalization of the site’s geological and hydrogeological conditions. To drastically cut the computational burden incurred during iterative inversion, high-quality datasets are generated via parameter sensitivity analysis and Latin hypercube sampling, based on which a deep convolutional neural network (DCNN)-driven high-fidelity surrogate model is constructed and embedded into the optimization framework as an equality constraint. A separated nonlinear programming model is formulated to independently quantify pollution source characteristics and hydrogeological parameters, with the objective of minimizing the residual error between field-measured and numerically simulated contaminant concentrations. A hybrid homotopy-particle swarm optimization (HH-PSO) algorithm is further proposed to address the limitations of conventional optimizers, including strong dependence on initial guesses and susceptibility to local optima. On this basis, a closed-loop feedback iteration scheme is developed, where source identification and parameter calibration are implemented alternately with bidirectional constraints and progressive correction to continuously refine and stabilize inversion outputs. This work presents distinct innovations in the methodology, algorithm, and practical application of DNAPL groundwater source inversion. Results from synthetic benchmark cases and on-site field applications demonstrate that the DCNN surrogate model achieves far higher fitting accuracy than shallow learning approaches (e.g., Kriging and support vector regression), with the coefficient of determination R² exceeding 0.99. After the feedback correction iteration procedure, the average relative error for retrieved source locations, release histories, and hydrogeological parameters drops to 3.72%, and the overall computational efficiency is elevated by approximately 99.84%. The integrated simulation–optimization inversion framework proposed in this work integrates monitoring signal denoising, multiphase numerical simulation, deep learning surrogate modeling, hybrid intelligent optimization, and feedback iterative correction. This integrated system effectively resolves core technical bottlenecks in DNAPL groundwater source inversion, such as nonlinear ill-posedness, equifinality induced by mutual interference between source terms and aquifer parameters, prohibitive computational costs of multiphase simulations, and premature convergence of traditional optimization algorithms. The established framework can serve as a robust theoretical foundation and technical tool for rapid, precise source tracing, pollution liability confirmation, and remediation design at complex contaminated sites. Full article

The sensitivity of classical electrochemical biosensors is constrained by noise processes at the electrode–electrolyte interface: low-frequency 1/f noise, thermal noise and capacitance fluctuations degrade the signal-to-noise ratio in ways that circuit-level mitigation reduces but does not remove. Chirality-Induced Spin Selectivity (CISS) has been proposed as a route past that limit, by shifting transduction from the scalar quantity of charge to the vector property of electron spin. Spin polarizations of up to approximately 60% have been reported at room temperature for double-stranded DNA monolayers in spin-resolved photoemission, while spin-dependent electrochemistry on smaller chiral adsorbates typically yields values in the range of about 5–30%; the reported magnitude is therefore system-, geometry-, technique- and analysis-dependent rather than a universal property of biological helices. Analyte binding modulates this efficiency through changes in helical pitch, dipole and structural integrity. This review unites the physics of CISS with the surface chemistry of spin-selective sensor layers, compares the competing mechanistic accounts of the effect, and then examines a persistent quantitative gap: the polarizations obtained from first-principles transport calculations on isolated chiral molecules remain well below the measured values. Non-relativistic, spin-restricted calculations on closed-shell helices in vacuum yield no polarization by construction, and although spin-polarized and relativistic implementations that treat spin–orbit coupling explicitly are available, they typically still underestimate experiments by orders of magnitude. We argue that a substantial part of this deficit is attributable to the widespread use of static, vacuum-based or implicitly solvated models, and that multiscale quantum mechanics/molecular mechanics (QM/MM) frameworks with explicit solvents are one necessary—though probably not sufficient—correction. Full article

The increasing availability of construction data and advances in artificial intelligence (AI) have accelerated the adoption of time series forecasting across construction management. However, a comprehensive understanding of the field’s knowledge structure, methodological evolution, and future directions remains limited. To address this gap, a scientometric and qualitative review was conducted on 192 journal articles published between 2010 and December 2025 and retrieved from the Web of Science Core Collection and Scopus databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. VOSviewer was employed to visualize the knowledge structure, collaboration networks, and research themes. The results indicate sustained growth in research activity since 2010, accompanied by increasing international collaboration. Six major research streams were identified: cost estimation and forecasting, safety and risk management, schedule and performance monitoring, productivity and resource management, sustainability and waste management, and emerging methods and future technological directions. The findings reveal a clear transition from traditional statistical approaches, including AutoRegressive Integrated Moving Average (ARIMA) and vector error correction (VEC) models, toward machine learning, deep learning, and hybrid forecasting frameworks. At the same time, traditional methods remain important because of their interpretability and practical applicability. Three persistent challenges were identified: data quality and availability, model interpretability, and practical implementation. Future research is expected to focus on lightweight real-time forecasting, multimodal data fusion, explainable AI, and physics-informed forecasting models. This review provides an integrated understanding of the field and a research agenda for future methodological and practical development. For practitioners, it further highlights that the value of forecasting models depends not only on predictive accuracy but also on interpretability, computational efficiency, data requirements, and practical deployability in construction decision-making. Full article

Reliable collision-risk warning during low-speed ship docking requires accurate and efficient hydrodynamic prediction. This study develops a physics-SVR (support vector regression) framework combining a three-degree-of-freedom maneuvering model with three SVR models that learn residuals in longitudinal force, lateral force, and yaw moment from computational fluid dynamics (CFD) data. The corrected loads support 120 s trajectory prediction and hull-envelope reconstruction. Minimum lateral and longitudinal clearances and their times to safety-threshold crossing distinguish normal, warning-alert, and emergency-alarm states. The database comprises 20 model-development conditions and three condition-level holdout tests representing unseen loading/draft, heading, and lateral-offset conditions. Across the holdout tests, overall relative errors decrease from 22.00–30.20% for the baseline model to 14.91–19.93%. In two hazardous cases, warning alerts precede contact by 154.9 and 172.09 s, while the non-hazardous control case triggers no alarm. The complete prediction-and-warning update requires 0.32 s on average, demonstrating potential for shore-based docking assistance under calm-water conditions. Full article

Understanding niche differentiation and interspecific coexistence patterns of macrobenthos provides critical references for ecological assessment of bivalve aquaculture. Seasonal field surveys (Autumn 2023–Summer 2024) across 12 sampling sites were conducted in an intensive Meretrix meretrix tidal flat farm in Rudong, Jiangsu. Synchronous water depth, sediment grain size and total organic carbon (TOC), and salinity were measured at each station to characterize habitat gradients; no unfarmed reference tidal flats were included, so all community patterns only reflect within-farm variation. The study area has sustained 10-year continuous bottom culture with baseline juvenile stocking density of 250 ind./m² and twice-yearly manual harvesting. A total of 58 macrobenthic taxa were identified, and 13 dominant species were screened with a dominance index threshold Y > 0.02. All abundance data were fourth-root-transformed before Bray–Curtis community analysis to reduce bias of hyper-abundant taxa. Standardized Shannon niche breadth (0–1 normalized, r = 48 site–season composite units) and permutation-tested (999 replicates) Pianka niche overlap/similarity coefficients were calculated to quantify resource utilization similarity. Niche breadth ranged from 0.018 to 0.971, with farmed M. meretrix showing the maximum value, indicating broader seasonal resource adaptation. High niche overlap (>0.50) was detected between M. meretrix and four wild dominant species (Nephtys californiensis, Moerella iridescens, Lingula anatina, Umbonium thomasi). Notably, niche metrics derived solely from spatial abundance data cannot directly confirm interspecific competition or trophic segregation; the stable coexistence observed in our assemblage is tentatively hypothesized to be driven by vertical sediment partitioning and divergent feeding guilds based on published functional studies, without direct in situ trophic or stratified sediment evidence from this work. Hierarchical UPGMA clustering (0.35 Bray–Curtis distance threshold) and corrected NMDS ordination (48 site–season units, dominant-species fitted vectors) illustrated distinct functional groups. Based on the observed niche convergence among filter-feeding bivalves under local farming conditions, the range of 230–280 ind./m² is presented as an observational reference. Controlled manipulative experiments with gradient stocking densities are needed to test whether this range can balance aquaculture yield and benthic biodiversity. Full article

This paper presents an automated computational workflow for the acquisition, instrument-response correction and three-dimensional visualization of multi-component seismic records obtained from the IRIS Federation of Digital Seismograph Networks (FDSNs) using the open-source ObsPy (v1.5.0) package. The workflow targets engineering applications and consists of four stages: (i) selection of three-component (3C) broadband stations, (ii) bandpass filtering and spectral deconvolution of the instrument response to obtain ground displacement in physical units, (iii) calculation of theoretical P- and S-wave arrival times with the Tau-P kinematic algorithm based on the IASP91 reference Earth velocity model, and (iv) construction of an interactive 3D particle motion visualization in which segments associated with the P-wave, S-wave and background are color-coded. The pipeline is demonstrated on three seismic events recorded in February 2023 by the broadband station KO.BNN, including the destructive Mw 7.8 Kahramanmaraş earthquake. The workflow yields the absolute three-dimensional displacement vector and produces interactive visualizations that are intended for use by structural engineers as a complement to traditional one-dimensional acceleration records. Full article

The increasing penetration of inverter-based renewable energy resources is reshaping transmission-line fault characteristics and weakening protection criteria designed for synchronous-generator-dominated grids. This paper proposes an internal-fault identification scheme based on voltage–current coupling characteristic curves (UICs) constructed from voltage and current measurements at both line terminals. Geometric descriptors of the UIC are used to build an ellipsoidal feature space representing normal operating conditions and external faults. Internal faults are identified from the normalized distance between the online feature vector and this space. A local voltage-transient startup criterion is also introduced, and current-transformer (CT) saturation correction is incorporated to reduce distortion in the measured currents. PSCAD simulations under different fault locations, transition resistances, fault types, noise levels, and CT-saturation conditions show that the proposed scheme distinguishes internal faults from external faults and normal operation reliably. Because the criterion depends on line-side coupling features rather than the short-circuit output of a specific power source, it is suitable for protection applications in renewable energy systems with grid-forming inverters. Full article

Marine fishery system resilience supports ecological and productive functions under interacting environmental and socioeconomic pressures. We assessed resilience and forecastability using 117 region–year observations from nine Chinese coastal provincial-level regions during 2011–2023. A fixed-reference Resistance–Adaptability–Recovery index combined equal and entropy weights calibrated on 2011–2018 data. We evaluated autocorrelation-adjusted trends, measurement sensitivity, regional score distributions, conditional associations using geographically and temporally weighted regression (GTWR), and rolling-origin forecast performance. The mean index increased from 0.307 in 2011 to 0.426 in 2023, and eight regions retained significant monotonic increases after false-discovery-rate correction. Alternative weighting, calibration, indicator-deletion, and aggregation specifications preserved the positive temporal direction while producing moderate rank variation. GTWR yielded the lowest in-sample AICc among the three regression specifications, supporting a spatiotemporally varying representation of conditional associations. Under rolling-origin testing, particle swarm optimization-tuned support vector regression produced an RMSE of 0.0318, whereas persistence achieved the lowest RMSE (0.0246) and lower region-level RMSE in seven of nine regions. Accordingly, the 2024–2030 results were reported as conditional scenarios. Eight regional specification ranges crossed zero; Guangdong remained positive across the nine specified trend-window and weighting combinations. The framework improves temporal comparability and separates historical assessment, spatial association analysis, and forecast evaluation. Full article

Assessing visual fatigue is crucial in eye-controlled interaction. Traditional methods are either overly subjective or rely on highly invasive, costly equipment and complex procedures that require expert supervision. This study proposes a machine-learning-based approach for visual fatigue assessment. Data collection employs non-intrusive, easily monitored eye-tracking to capture ocular eye movement data and task performance data, while subjective questionnaires label fatigue states. For feature selection, participant-level Wilcoxon signed-rank tests with Benjamini–Hochberg FDR correction were used to identify fatigue-related indicators, and a redundancy-removal step based on Spearman correlation yielded a final set of six non-redundant features. For the assessment method, we introduced a gradient boosting decision tree–logistic regression (GBDT-LR) model whose hyperparameters are optimized via Bayesian optimization. All models were evaluated under a unified 5-fold stratified cross-validation framework with within-fold standardization and nested hyperparameter tuning. Results indicate that this model can effectively predict the state of visual fatigue. Compared with the performance of five other models—gradient boosting decision tree (GBDT), logistic regression (LR), support vector machine (SVM), random forest (RF), and RF-SVM—the proposed GBDT-LR model achieved an assessment accuracy of 89.79%, demonstrating strong predictive performance. This study provides an effective method for predicting visual fatigue in eye-controlled interaction, laying a research foundation for optimizing the user experience of eye-controlled interaction and promoting the sustainable development of eye-control technology. Full article

Gene therapy and genome editing increasingly depend on the safe, effective, and cell-selective delivery of nucleic acids and protein–nucleic acid complexes. Although viral vectors remain important for applications requiring durable gene expression, non-viral vectors offer advantages in cargo capacity, modularity, transient expression, potential repeat dosing, and avoidance of vector–genome integration. Lipid nanoparticles (LNPs), polymeric nanoparticles, inorganic nanomaterials, extracellular vesicles (EVs), and biomimetic hybrid systems have consequently become central platforms for delivery of siRNA, mRNA, plasmid DNA, antisense oligonucleotides, and CRISPR-based genome editors. Among these, ionizable LNPs are currently the most clinically mature non-viral technology, supported by the clinical success of siRNA therapeutics and mRNA vaccines, as well as the emergence of in vivo CRISPR therapies. Nevertheless, efficient endosomal escape, cell-type-selective targeting, extrahepatic delivery, and repeat-dose tolerability remain substantial barriers. Polymeric vectors provide broad chemical tunability, allowing adjustment of charge density, degradability, stimulus responsiveness, intracellular trafficking, and cargo release. However, toxicity and batch-to-batch reproducibility remain key concerns. EVs provide a biologically derived alternative with favorable membrane interfaces and potential advantages for protein and ribonucleoprotein delivery, but their clinical translation is constrained by heterogeneity, loading efficiency, product characterization, and scalable manufacturing. This review summarizes recent advances in non-viral gene-delivery platforms, compares their strengths and limitations, and discusses future directions in cell-selective delivery, endosomal escape, transient delivery of genome-editing machinery, engineered EVs, hybrid vectors, and manufacturing-oriented development. The field is transitioning from organ-level delivery toward delivery of the correct payload to the correct cell type at a clinically relevant exposure and safety margin. Full article

The Coordinate Rotation Digital Computer (CORDIC) algorithm is widely employed in digital signal processing and hardware accelerators because it computes a broad range of elementary functions using iterative shift-and-add operations. Conventional CORDIC implementations, however, execute a fixed number of iterations irrespective of the input characteristics or the precision required, resulting in unnecessary computational overhead and increased execution latency. This work presents an explainable machine learning framework for adaptive iteration optimization in a multi-mode CORDIC architecture supporting circular, hyperbolic, and linear operating modes. A unified prediction framework for calculating the optimal number of iterations is made possible by the developing a generic feature representation to describe the numerical behavior of CORDIC computations across various modes. We systematically evaluated eight regression models, including Linear Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Regression, Multi-Layer Perceptron, and Extreme Gradient Boosting (XGBoost) and LightGBM. Among the models evaluated, the Decision Tree achieved the best performance on an independent test set of 2305 samples from 461 previously unseen input groups, with a MAE of 0.9160 iterations, RMSE of 1.9671, and $R^2$ of 0.6076. Predictions were within one and two iterations of the reference value for 80.26% and 90.07% of the test samples, respectively. Since prediction accuracy alone does not guarantee that the required numerical tolerance will be satisfied, the predicted iteration count was further evaluated using the actual CORDIC error, followed by a safety-correction procedure. The safety-corrected approach achieved 100% tolerance satisfaction on the independent test set, reducing the mean number of iterations from 20 to 11.739, corresponding to a 41.31% reduction in iterations. Model behavior was further interpreted using feature importance analysis, permutation importance, and feature ablation studies to examine the contribution of individual features to iteration prediction. Statistical robustness is established using bootstrap confidence intervals, the Friedman test, and Holm-corrected Wilcoxon signed-rank tests. Full article