Search Results (712)

To tackle the degraded stability and non-smooth mode transitions under wide-range grid-strength variations with high renewable penetration, an adaptive fusion and disturbance-free switching control strategy is proposed, where parameter stability regions are analyzed using the D-partition method, thereby improving robustness over single-mode grid-following/grid-forming operation and reducing transients from conventional switching. First, a unified frequency-domain characteristic equation that incorporates the fusion weight is derived based on the sequence-impedance stability criterion, providing a consistent theoretical basis for stability modeling and assessment across operating conditions. Next, under wide-range grid-strength conditions, the controller-parameter stability region is computed subject to multiple constraints, including phase margin, gain margin, and short-circuit ratio, and the resulting robust feasible set is geometrically characterized on the parameter plane. Furthermore, to suppress transient disturbances induced by variations of the fusion weight with grid strength near the switching threshold, a D-zone-based multi-partition, stage-by-stage smoothing adaptive fusion strategy is developed. A nonlinear weight mapping yields a continuous transition trajectory, enabling seamless, disturbance-free transitions from weak to strong grids. Finally, simulation and experimental results quantitatively validate the superiority of the proposed method. Under severe weak-grid conditions with a short-circuit ratio of 1, the fusion strategy enlarges the parameter-stability feasible region by approximately 11.5% compared to single-mode operations. Moreover, the proposed D-zone strategy achieves a peak fusion advantage ratio of 43.11%, ensuring robust and disturbance-free switching across a wide range of grid-strength scenarios where the short-circuit ratio varies from 1 to 30. Full article

The transition towards a circular bioeconomy in developing regions is frequently hindered by operational failures caused by feedstock discontinuity. Whilst biochemical potential is traditionally the primary selection criterion, this study postulates that logistic reliability serves as the governing constraint. To validate this strategic reorientation, a decision-making framework was developed and applied to a representative tropical agro-industrial region. A sensitivity analysis comparing objective, subjective and neutral weighting scenarios identified annual residue production as the dominant factor. Results established cattle manure as the universal baseload substrate essential for mitigating seasonality, outweighing higher-yielding but intermittent agricultural residues. Spatial analysis revealed distinct territorial vocations, identifying a high-availability rice–livestock cluster in the south suitable for centralised industrial plants and dispersed cassava–livestock nodes in the centre favourable for decentralised digestion. Furthermore, the assessment of energy autonomy demonstrated that the prioritised co-digestion scenarios could cover local residential electricity demand between 1.5 times and 81 times. Crucially, residues favoured by expert judgement proved logistically unfeasible despite superior theoretical yields. This data-driven approach demonstrates that successful substrate selection must transcend theoretical yield maximisation to prioritise supply chain reliability, providing a robust roadmap for de-risking bioenergy investments and ensuring regional energy autonomy. Full article

Linear vector fields on Lie groups constitute a fundamental class of dynamical systems, as their flows are one-parameter subgroups of automorphisms and their infinitesimal behavior is entirely determined by derivations of the Lie algebra. When a Lie group is endowed with a Hamiltonian-type geometric structure, a natural problem is to determine whether such linear dynamics admit a global variational realization, and how such realizations can be interpreted in terms of reduced models of fluid motion. In the even-dimensional case, where the Lie group carries a symplectic structure, we establish a complete global criterion for the existence of Hamiltonians generating linear symplectic vector fields. The problem reduces to a single global obstruction: the de Rham cohomology class of the 1-form ${\iota }_{\mathcal{X}}\omega$. Thus, every linear symplectic vector field on a simply connected Lie group is globally Hamiltonian, and when the obstruction vanishes, we provide an explicit constructive procedure to recover the Hamiltonian. On the affine group ${\mathrm{Aff}}^{+}\left(1\right)$, this yields a fully explicit, finite-dimensional Hamiltonian model of a 1D ideal fluid with affine symmetries. We then treat odd-dimensional Lie groups, where symplectic geometry is unavailable. Using contact geometry as the canonical replacement, we prove a Hamiltonian lifting theorem ensuring the existence and uniqueness of the associated dynamics. The Reeb vector field appears as a distinguished vertical direction resolving the ambiguities of degenerate Hamiltonian systems. On the Heisenberg group $H_3$, this gives a fully explicit contact Hamiltonian model of an effective non-conservative fluid mode. Finally, we interpret symplectic and contact theories within a unified geometric framework and discuss their relevance to geometric formulations of ideal (symplectic) and effective (contact) fluid equations on Lie groups. Full article

Maintaining frequency regulation in interconnected power systems becomes increasingly difficult in the presence of nonlinear operating conditions. To address this issue, this study develops a hybrid load frequency control scheme in which a fuzzy fractional-order FOPI–FOPD controller is incorporated within a PIDF framework for a two-area LFC system. The controller parameters are optimized using the Dwarf Mongoose Optimization Algorithm (DMOA) and the Catch Fish Optimization Algorithm (CFOA), while the Integral of Time-Weighted Absolute Error (ITAE) is adopted as the performance criterion. The proposed strategy is examined under both linear and nonlinear scenarios, including the effects of Governor Dead Band (GDB) and Generation Rate Constraints (GRC). In the linear case, the DMOA-based design achieves an ITAE of 0.02939 with a tie-line settling time of 13.5478 s, whereas the CFOA-based design produces a bounded and convergent response with an ITAE of 0.03937 and a settling time of 14.4947 s. When GDB nonlinearity is introduced, the DMOA-tuned controller exhibits performance deterioration, yielding an ITAE of 0.1098 and a settling time of 19.0416 s, while the CFOA-tuned design shows more favorable time-domain performance with a lower ITAE of 0.05845 and a bounded settling time of 16.3595 s. These findings indicate that the CFOA-optimized PIDF–Fuzzy FOPI–FOPD controller provides an effective LFC solution under the examined nonlinear operating conditions. Full article

Maintaining ballast performance in seasonal frozen regions is essential for resilient and sustainable railway infrastructure because freeze–thaw-driven fouling can shorten service life and increase maintenance-related material consumption. To investigate the deterioration mechanisms of fouled railway ballast in seasonal frozen regions, freeze–thaw cycle tests and cyclic loading model tests were conducted in sequence using a custom low-temperature geotechnical system. The test results processed by Origin software indicate that unfrozen water migrates toward the freezing front under temperature gradients and forms ice lenses during freezing. During thawing, meltwater is retained above the underlying frozen soil. Repeated freeze–thaw cycles therefore promote progressive water accumulation in the upper soil layers, eventually forming a clay layer with high water content. Under cyclic loading, interlayer thickening exhibited clear moisture thresholds relative to the clay liquid limit (LL = 24%). Below the LL (18–24%), ballast penetration and fines migration were limited and thickness increased slowly. Above the LL, rapid strength loss accelerated penetration and upward transport. At an initial water content of 32%, fines migration surpassed the ballast surface and the ballast became fully fouled, meaning that the fouled interlayer thickness equaled the full 100 mm ballast-layer thickness. Fouling severity increased sharply with moisture: the void contaminant index exceeded the maintenance criterion (VCI > 40%) at 28% water content and evolved into severe mud pumping at higher concentrations. Excess pore water pressure developed stratification with depth, maintaining an upward hydraulic gradient near the interface and yielding a net water loss of 2.24–6.91% in the upper fine-grained layer. These quantified thresholds and mechanistic insights provide actionable trigger points for condition-based maintenance and climate-adaptive design, helping extend track-bed service life and reduce resource-intensive ballast renewal in seasonal frozen regions. Full article

Background: Combat-related lower extremity injuries frequently require prolonged tourniquet application to control life-threatening hemorrhage. Although effective for hemorrhage control, prolonged ischemia followed by reperfusion substantially increases the risk of rhabdomyolysis, acute kidney injury (AKI), limb loss, and mortality. The optimal timing of anti-rhabdomyolysis infusion therapy in relation to tourniquet release remains uncertain. Methods: This retrospective single-center cohort study analyzed 120 Ukrainian military casualties with combat-related lower extremity injuries requiring prolonged tourniquet application and subsequent surgical management, including fasciotomy and tourniquet release. Patients were divided into two groups based on infusion strategy: standard therapy initiated after tourniquet release and early anti-rhabdomyolysis infusion therapy initiated before tourniquet removal during the ischemic phase. Primary outcomes included dialysis-requiring AKI, limb amputation, and death. Multivariable logistic regression models were adjusted for baseline physiological severity, including shock index at admission and baseline acid–base status. Model performance was evaluated using the Akaike Information Criterion (AIC) and receiver operating characteristic (ROC) analysis. Propensity score–based inverse probability of treatment weighting (IPTW) was applied as a sensitivity analysis. Results: After adjustment, early infusion therapy was independently associated with lower rates of dialysis-requiring AKI (adjusted odds ratio [OR] 0.33; 95% confidence interval [CI] 0.13–0.84; p = 0.020), limb amputation (OR 0.32; 95% CI 0.11–0.95; p = 0.040), and mortality (OR 0.23; 95% CI 0.07–0.77; p = 0.017). Adjusted models demonstrated good discriminative ability, with areas under the ROC curve of 0.813 for AKI, 0.838 for amputation, and 0.823 for mortality. Sensitivity analyses using IPTW yielded consistent results. Conclusions: In combat-related lower extremity injuries requiring prolonged tourniquet application, early initiation of anti-rhabdomyolysis infusion therapy prior to reperfusion is associated with significantly reduced risks of severe AKI, limb loss, and death. These findings suggest that preventive renal-protective strategies initiated before tourniquet release may improve outcomes in high-risk military trauma settings and warrant further prospective investigation. Full article

Heike 60, a cold-tolerant soybean cultivar developed at the Heihe Branch of the Heilongjiang Academy of Agricultural Sciences, was evaluated across seven locations in Heilongjiang Province, northeastern China, over four growing seasons (2015–2018), generating 28 site–year environments. The objectives were to characterize yield performance and stability, partition sources of agronomic variation, and identify the yield component pathways through which the cultivar adapts to contrasting cold–temperate environments. Grain yield across the trial network ranged from 1591 to 3219 kg ha⁻¹ with a grand mean of 2688 kg ha⁻¹, and Heike 60 consistently outperformed the regional check variety Heihe 43 across all evaluated locations and seasons, with a mean yield advantage of 11.5%. Two-way ANOVA revealed highly significant (p < 0.001) Year, Location, and Year × Location interaction effects for all eight agronomic traits examined, with the interaction term accounting for the largest proportion of yield variance, indicating that relative site performance was not consistent across seasons. Five of the seven locations were classified as stable by the coefficient of variation criterion (CV < 15%), with Eberhart–Russell regression coefficients of 1.000 across all sites confirming average and proportional responsiveness to environmental quality. Hierarchical cluster analysis partitioned the 24-core site–year environments into three agronomically distinct groups reflecting differences in accumulated thermal resources: a pod number-compensating profile under lower temperature accumulation, a seed weight-dominated profile under higher post-anthesis thermal supply, and a balanced yield component expression representing the predominant growing conditions of the region. Random forest modeling identified hundred-seed weight, pods per plant, and growth period as the primary predictors of grain yield across environments. Collectively, the results demonstrate that Heike 60 possesses broad adaptability and phenotypic plasticity across the cold–temperate soybean production zone of Heilongjiang Province, combining competitive mean yield with stable performance across diverse environmental conditions. Full article

Precise timing of rice harvesting is critical for ensuring grain yield and quality. Traditional manual evaluation methods are highly subjective and time-consuming, highlighting the critical demand for rapid, non-destructive approaches to estimate rice maturity. This study focused on cold-region japonica rice grown in Heilongjiang Province, aiming to develop and validate dual-scale (pot and field) maturity estimation models. For model development, canopy spectral data were collected using two complementary acquisition tools: a ground-based active sensor (CGMD402) and UAV-borne multispectral imagery. Four modeling algorithms—Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were utilized, with input variables comprising single spectral indices (Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI) and composite spectral indices (Normalized Difference Maturity Ratio Vegetation Index, NDMRVI; Normalized Difference Pigment Ratio Vegetation Index, NDPRVI). At the pot scale, composite spectral indices showed stronger correlations with rice maturity than single indices. Among the four algorithms, the DT model with combined NDVI + RVI input yielded the optimal comprehensive performance, with a coefficient of determination (R²) of 0.957, a root mean square error (RMSE) of 0.064, and a relative error (RE) of 4.8% in the test set. At the field scale, NDVI and RVI both exhibited strong negative correlations with maturity (Spearman’s correlation coefficients of −0.76 and −0.79, respectively). While the RF model performed best in the training set (R² = 0.752), it was prone to overfitting; in contrast, Multiple Linear Regression (MLR, Ridge Regression) with NDVI + RVI combination demonstrated greater stability in the test set (R² = 0.515, RMSE = 0.116). Notably, composite spectral indices consistently outperformed single indices across all modeling algorithms, but their accuracy was comparable to the optimal single index combination model. To tackle the challenge of scaling models from pot to field conditions, this research developed a “modeling–validation–evaluation–scaling” framework and a four-indicator combined judgment criterion (ΔR²–ΔRMSE–ΔRE–SF). Quantitative analysis showed that the optimal pot-scale model suffered significant accuracy loss during cross-scale transfer: ΔR² = 0.447, ΔRMSE = 0.120, ΔRE = 22.84%, and Scale Transfer Factor (SF) = 2.875. A “regional calibration + residual correction” scheme was proposed, which is expected to reduce the transferred RMSE to below 0.12 and SF to 1.8–2.0. Overall, this research offers a reliable technical method for large-scale, non-destructive monitoring of rice maturity, which can facilitate data-driven precision harvesting decisions. Full article

This study systematically investigates the reaction characteristics of laminated shale oil reservoirs in the 7₃ sub-member of the Yanchang Formation, Heshui area, Ordos Basin, under exposure to CNI-I nanoviscous fracturing fluid. The reservoir matrix comprises 84.85% brittle minerals and 15.15% clay minerals. Fluid–rock interactions significantly dissolve calcite and dolomite, releasing Ca²⁺ and Mg²⁺ ions, while clay mineral reactions liberate substantial amounts of Na⁺. Post-reaction, fluid system stability is markedly reduced, elevating the risk of precipitate formation and pore-throat plugging. Exposure to fracturing fluid reduces the T₂ cutoff value of core samples from 3.29 ms to 1.72 ms, indicating a densification of the micro-pore-throat network and a decline in mobile fluid saturation, while fracture apertures exhibit widening. Based on empirical data, a discriminant criterion (R value) defined as the ratio of fracture aperture increment rate to pore-throat diameter reduction rate is established at 1.25, confirming that fracture propagation dominates over pore constriction. Dual-medium modeling yields a net permeability enhancement of 19.35%. Fluid–rock interactions induce overall degradation of rock mechanical properties with pronounced anisotropy: rock strength along the direction perpendicular to bedding declines by 37.546%, Young’s modulus decreases by 1.81%, and Poisson’s ratio increases by 0.02%—all significantly exceeding the degree of degradation parallel to bedding. This anisotropic mechanical degradation predisposes the near-wellbore region to shear slip and wall spalling, prompting the development of targeted engineering mitigation strategies. Full article

We study a parsimonious constrained two-component Gaussian mixture with symmetric locations $\pm \lambda$ and unequal weights controlled by $\alpha \in [-1,1]$; we refer to this family as the asymmetric bimodal normal. The constraint eliminates label switching and yields an identifiable parametrization for $\lambda >0$, while noting the boundary degeneracy at $\lambda =0$ where $\alpha$ is not identifiable. We derive closed-form analytical expressions for the density and distribution functions, an equivalent constructive representation (useful for simulation and interpretation), explicit moment formulas, and conditions distinguishing unimodality from bimodality. For inference, we develop maximum likelihood estimation with observed information standard errors and provide numerically stable fits via a block-coordinate quasi-Newton routine using method of moments initial values. A Monte Carlo simulation study across representative parameter settings evaluates bias and root mean squared error, and examines the behavior of Hessian-based standard error estimates, highlighting regimes where the observed information becomes ill-conditioned under weak separation. Empirical analyses, chemical calibration deviations from the National Institute of Standards and Technology and a regression example with asymmetric errors, show competitive or superior fit and interpretability relative to skewed normal alternatives, asymmetric Laplace models, and unconstrained Gaussian mixtures, with consistent advantages under model comparison using the Akaike information criterion and the Bayesian information criterion. Full article

The continuous growth in both the number of phenotypic records and the range of traits included in beef cattle genetic evaluations poses substantial statistical and computational challenges for the estimation of genetic and residual (co)variance matrices required for breeding value estimation. Structural equation models (SEM), implemented using either factor analysis (FA) or recursive model (REC) structures, provide a flexible framework to model genetic and residual (co)variance matrices while yielding more parsimonious and computationally efficient parameterizations. Here, SEM was applied to estimate parameters for growth and ultrasound-measured carcass traits in beef cattle. The dataset comprised 2942 animals, and six traits were evaluated using standard multiple-trait mixed models (SMTM) and SEM. We considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Relative to the SMTM, both the model with two factors in the genetic covariance matrix (FA2G) and the model in which six recursive effects were constrained to zero in the residual covariance matrix (REC1) demonstrated a strong ability to capture genetic variability, as reflected by comparable heritability estimates. Correlations between estimated breeding values (EBV) for the same traits across models were consistently high, ranging from 0.94 to 1.00, indicating strong agreement among model estimates. The FA2G model was the most parsimonious in terms of the effective number of parameters ($p_D$), with 431.2 $p_D$, corresponding to a reduction of 25.3 parameters relative to the SMTM. The REC1 model also emerged as a competitive alternative for this dataset, exhibiting a lower $p_D$ (443.6) than the SMTM (456.5) and the most favorable deviance information criterion among all models evaluated (e.g., 37,868.6 for REC1 versus 37,874.7 for SMTM). Overall, these results demonstrate that mixed-effects multi-trait models for beef cattle genetic evaluation can be effectively implemented using FA or REC structures, which provide parsimonious representations of the underlying covariance patterns while maintaining high agreement in EBV. Full article

Regional flood frequency analysis (RFFA) is a cornerstone for estimating design floods at ungauged or data-scarce sites by pooling information within hydrologically homogeneous regions. This study proposes and evaluates a hybrid RFFA framework that integrates the Index-Flood (IF) technique with a bivariate logistic extreme-value model whose marginal distributions are formulated under both stationary and non-stationary assumptions. Non-stationarity is incorporated through a covariate-dependent location parameter, using time and large-scale climate indices—the Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI)—as explanatory variables. The proposed approach is applied to two contrasting hydrological regions in Mexico—RH10 (Sinaloa) and RH23 (Chiapas Coast)—to assess its performance under differing climatic and hydrological regimes. Model adequacy and stability are evaluated using likelihood-based goodness-of-fit criteria (log-likelihood and Akaike Information Criterion) and a leave-one-out (jackknife) cross-validation scheme embedded within the IF regionalization workflow. Results indicate that non-stationary bivariate formulations dominate model selection at most stations and yield stable regional growth curves, providing robust and engineering-relevant performance under cross-validation. Overall, the proposed framework offers a conservative and operational pathway for regional flood quantile estimation that bridges local data scarcity and regional hydrological characterization in environments influenced by climate variability and long-term change. Full article

Herein, RATSGO (Real-time Automated Training and Sensing for Gas Odor), a fully automated live-animal olfactory training platform, for the detection of GBL as a sexual assault-facilitating drug is reported. The system integrates four distinct operant conditioning-based training paradigms, all executed without human intervention, to enhance learning speed, consistency, and scalability. Using this fully automated framework, four rats were trained to identify γ-butyrolactone (GBL). Three of the four animals successfully reached the predefined learning completion criterion, whereas one failed to meet the criterion. Across 320 automated trials, the GBL rats achieved a mean detection accuracy of 90%, with sensitivity and specificity values of 97% and 82%, respectively. The corresponding positive and negative predictive values (PPV and NPV) were 85% and 96%. When challenged with GBL diluted in drinking water (180 trials), performance remained high, yielding 88% accuracy, 89% sensitivity, 87% specificity, 85% PPV, and 90% NPV. Similarly, in experiments involving GBL mixed with whisky (200 trials), the rats demonstrated robust recognition capability, achieving 90% overall accuracy, perfect sensitivity (100%), 84% specificity, 79% PPV, and 100% NPV. Importantly, odor discrimination performance was preserved when reassessed four months after the completion of training, indicating strong long-term retention of the learned odor representations. Collectively, these findings confirm that the RATSGO system supports rapid, stable, and precise odor learning, underscoring its promise as a practical and extensible biological sensing platform for chemical detection applications. Full article

Speed management plays a critical role in road safety; however, conventional speed limits are determined based on characteristics such as geometry and traffic volume. Limited consideration is given to the structural condition of pavements and surface distress. This study proposes an integrated mechanistic–quantitative framework that links pavement distress and road safety indicators to the selection of speed limits. A flexible pavement section on Highway No. 80 in Iraq is analyzed as a case study. Mechanistic pavement analysis using KENPAVE is employed to estimate critical strains based on field traffic data and Equivalent Single-Axle Loads (ESALs). The rate of failure is estimated by comparing ESALs and the allowable load repetitions. Safety-related constraints are then derived to quantify hydroplaning risk, braking performance through stopping sight distance, and the vertical shock criterion. The results indicate that the existing pavement structure is marginal, with a high probability of fatigue failure and sensitivity to rutting under increased traffic loads. The integrated safety analysis yields a critical wet-weather speed of approximately 67–70 km/h, while localized settlements exceeding 10 mm require speed reductions of 50–60 km/h to maintain vehicle stability. The proposed framework demonstrates that pavement conditions directly influence safe speed, providing a rational basis for safety-oriented speed management. Full article

As deep learning models become increasingly integrated into critical decision-making systems, the need for explainable Artificial Intelligence (xAI) has grown paramount to ensure transparency, accountability, and trust. Post hoc explainability methods, which analyse trained models to interpret their predictions without modifying the underlying architecture, have become increasingly important, especially in fields such as healthcare and finance. Modern xAI techniques often produce feature importance rankings that fail to capture the true causal influence of features, particularly in transformer-based models. Recent quantitative metrics, such as Symmetric Relevance Gain (SRG), which measures the area between the feature corruption performance curves of the Most Important Feature (MIF) and the Least Important Feature (LIF), provide a more rigorous basis for evaluating explanation fidelity. In this study, we first show that existing xAI methods exhibit consistently poor performance under the SRG criterion when explaining transformer-based text classifiers. To address these limitations, we introduceEvoDropX, a novel framework that formulates explanation as an optimisation problem. EvoDropX leverages Grammatical Evolution (GE) to evolve sequences of feature corruption with the explicit objective of maximising SRG, thereby identifying features that most strongly influence model predictions. EvoDropX provides interventional, input–output (behavioural) explanations and does not attempt to infer or interpret internal model mechanisms. Through comprehensive experiments across multiple datasets (IMDb movie reviews (IMDB), Stanford Sentiment Treebank (SST-2), Amazon Polarity (AP)), multiple transformer models (Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, DistilBERT), and multiple metrics (SRG, MIF, LIF, Counterfactual Conciseness (CFC)), we demonstrate that EvoDropX significantly outperforms all state-of-the-art (SOTA) xAI baselines including Attention-Aware Layer- Wise Relevance Propagation for Transformers (AttnLRP), SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), when evaluated using intervention-based faithfulness criteria. Notably, EvoDropX achieves $74.77\%$ improvement in SRG than the best-performing baseline on the IMDB dataset with the BERT model, with consistent improvements observed across all dataset-model pairs. Finally, qualitative and linguistic analyses reveal that EvoDropX captures both sentiment-bearing terms and their structural relationships within sentences, yielding explanations that are both faithful and interpretable. Full article