Search Results (1,225)

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

Background: Streptococcus suis serotype 2 is a major zoonotic pathogen responsible for severe systemic infections in pigs and humans, including septicemia, meningitis, and high mortality outcomes. Cefquinome, a fourth-generation β-lactam antibiotic widely used in veterinary medicine, is commonly applied for the treatment of S. suis infections. However, optimized dosing strategies remain insufficiently defined, particularly under conditions of varying bacterial burden, inoculum size, and reduced susceptibility or resistance phenotypes. These factors may significantly alter pharmacodynamic responses and compromise the predictive value of conventional MIC-based approaches. Objectives: This study aimed to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of cefquinome against S. suis serotype 2 using an integrated ex vivo serum time-kill experiments and semi-mechanistic PK/PD modeling. A secondary objective was to evaluate optimized dosing regimens across different inoculum levels and susceptibility phenotypes, including a cefquinome-resistant mutant. Methods: Cefquinome pharmacokinetics following intramuscular administration at 2 and 4 mg/kg in piglets were described using a two-compartment model. Dose proportionality, exposure linearity, and clearance parameters were assessed. Ex vivo serum time-kill experiments were conducted using a parental strain and a cefquinome-resistant mutant (M1) under normal-inoculum (NI), high-inoculum (HI), and mutant/resistant (MS) conditions. A semi-mechanistic PK/PD model incorporating logistic bacterial growth, sigmoidal Emax killing, nutrient limitation, and a time-delay function was developed to describe dynamic bacterial responses. Model parameters (k₀, k_max, EC₅₀) were estimated using nonlinear least-squares regression (Scientist v2.0), and simulations were performed by integrating time-varying PK input functions. Results: Cefquinome demonstrated linear pharmacokinetics with dose-proportional increases in Cmax and AUC between 2 and 4 mg/kg, with comparable clearance across doses. Ex vivo studies revealed time-dependent antibacterial activity with a pronounced inoculum effect. Higher bacterial burdens significantly reduced bactericidal efficiency and promoted regrowth during declining drug exposure. No tested concentrations achieved ≥3-log₁₀ killing in HI or MS conditions, whereas the NI group achieved a maximal reduction of 3.5-log₁₀ CFU/mL. MIC values in serum and medium were consistent (0.03, 0.06, and 0.24 µg/mL for NI, HI, and MS, respectively), indicating minimal protein binding influence. The semi-mechanistic model accurately described observed bacterial dynamics (R² > 0.99; MSC > 1.5), capturing delayed drug effects, inoculum-dependent growth suppression, and regrowth phenomena. Growth rates were reduced under serum conditions, reflecting nutrient limitation. Importantly, inoculum size exerted a stronger impact on pharmacodynamic outcomes than resistance phenotype, as reflected by reductions in kmax and increases in EC₅₀ under HI conditions. Although %T>MIC exceeded conventional β-lactam targets (>40%) in most regimens, MIC-based indices poorly correlated with observed dynamic killing responses. Conclusions: Cefquinome exhibited time-dependent antibacterial activity against S. suis serotype 2, strongly modulated by inoculum size and reduced susceptibility. The developed semi-mechanistic PK/PD model provided robust prediction of bacterial time-kill behavior and outperformed MIC-based metrics in guiding dose optimization. Simulation results support 2 mg/kg every 24 h for normal infections and 2 mg/kg every 12 h for high-inoculum or less susceptible infections, emphasizing the value of model-informed dosing strategies for optimizing β-lactam therapy. Full article

Pixel-level annotation of remote sensing imagery is costly, motivating weakly supervised semantic segmentation (WSSS) using only image-level labels. However, class activation maps (CAMs) often highlight only discriminative sub-regions and fail to separate adjacent land-cover regions, particularly in remote sensing scenes characterized by densely co-occurring land-cover classes and substantial variations in object scale. To address these limitations, we propose a three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training (CLIP), Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM). First, a lightweight CLIP adapter aligns vision–language priors with remote sensing imagery, while sigmoid-based multi-label decoupled distillation replaces class-competitive distillation with independent class-wise supervision, producing more complete CAMs. Second, DINOv2-guided feature clustering decomposes large merged regions before SAM prompt generation, while Spatial–Semantic Constraints are used to construct confidence-guided point-and-box prompts and reject excessively expanded or semantically inconsistent masks, thereby generating reliable pseudo-labels. Finally, a compact segmentation network is initialized with the weights learned in Stage 1 and retrained using the refined pseudo-labels generated in Stage 2, eliminating the need for foundation models during inference. Experiments on the Potsdam, LoveDA, and DeepGlobe datasets show that the proposed method achieves mean intersection over union (mIoU) scores of 53.16%, 52.66%, and 62.98%, respectively, outperforming state-of-the-art WSSS baselines by 6.55, 1.16, and 1.27 percentage points, respectively. These results demonstrate the effectiveness and generalizability of the proposed framework across diverse remote sensing scenarios under image-level supervision. Full article

This paper develops a Banach space-valued approximation framework based on symmetrized neural network (SNN) operators generated by a deformation-dependent sigmoidal activation function. Symmetry is introduced directly at the activation level through a reciprocal-deformation mechanism, yielding a positive, even, normalized, and localized density kernel satisfying the partition of unity. The resulting construction provides normalized compact-interval and whole-line quasi-interpolation operators for Banach space-valued functions. Quantitative pointwise and uniform convergence estimates are established through the first modulus of continuity and are extended to higher-order and Caputo–Bochner fractional approximation. Numerical diagnostics support the theoretical kernel properties, and fractional approximation experiments compare the SNN and classical NN operators under common computational conditions. A controlled blind-prediction experiment on a synthetic monthly temperature-like series uses a strict fit–validation–test protocol and a parameter-matched operator comparison, with seasonal ARIMA and MLP models as external baselines. Across five independent realizations, the SNN attains the best mean predictive performance, with $R^2=0.9500$, NMAE $=0.0452$, and NRMSE $=0.0570$. A vector-valued experiment in $Y={\mathbb{R}}^2$ further illustrates the non-scalar applicability of the Banach space framework. In addition, the normalized SNN kernel weights provide an intrinsic node-level interpretation mechanism without requiring an external post hoc explainability method. Full article

The moisture sorption isotherms of corn extrudates fortified with ultrafiltration (UF) concentrate derived from rose wastewater (RWW) were examined. Two formulations containing 4 g (sample S4) and 11 g (sample S11) of retentate, respectively, per 100 g of semolina were prepared and subsequently extruded. Adsorption and desorption isotherms of both samples were determined at 10 °C, 25 °C and 40 °C using the static gravimetric method within a water activity range of 0.11–0.85. The experimental data of equilibrium moisture content (EMC) were fitted using modified GAB, modified Oswin, and modified Halsey equations. The monolayer moisture content of the samples was determined using the BET model. The results showed that the sorption isotherms exhibited a typical sigmoidal (Type II) shape and a pronounced hysteresis effect between adsorption and desorption. The hysteresis effect decreased with increasing temperature. Based on the combined evaluation of error analysis and residual distribution, the modified Oswin model provided the most balanced overall performance and was determined as suitable for the description of the sorption isotherms of the extrudates studied. The effect of UF-retentate level on EMC was not uniform and depended on temperature, water activity, and sorption direction. The monolayer moisture content of the extrudates varied from 4.08 to 8.39% d.b. The higher retentate level was generally associated with higher monolayer moisture values. Packaging and storage at the theoretically estimated range of water activities from 0.09 to 0.24 may be expected to maintain EMCs close to the monolayer. Full article

Geo-information texts, including geospatial data-use regulations and Earth observation metadata, are central to data governance and compliance auditing in remote sensing ecosystems. Full fine-tuning of large pre-trained language models is often computationally impractical, while standard LoRA reduces cost but assigns a fixed rank to all adapted modules, ignoring differences across layers and projection types. This paper proposes TARA, a task-adaptive rank allocation method for LoRA-based fine-tuning. TARA assigns learnable importance scores to rank dimensions and uses Gumbel–Sigmoid sampling with the Straight-Through Estimator to learn discrete rank masks under a global sparsity constraint. We further construct RSRegulation, a geospatial regulatory compliance benchmark containing 4032 English-language samples derived from 168 clauses across seven regulatory and policy sources with clause-level data isolation. Across five random seeds, TARA achieves 95.30 ± 0.10% accuracy and 95.44 ± 0.10% F1 with a maximum trainable adapter budget of 1.57 M parameters. The learned soft allocation corresponds to approximately 0.38 M effective adapter parameters and 75.8% soft rank compression. Physical hard pruning reduces the deployed adapter to 0.086 M parameters while retaining 95.12 ± 0.11% accuracy and 95.21 ± 0.10% F1. Layer-wise analysis shows that value projections retain higher ranks than query projections under the current task and backbone, revealing a task-dependent non-uniform allocation pattern. Full article

Acerola is a tropical fruit rich in vitamin C and other bioactive compounds, but its high perishability limits storage and distribution. This study evaluated the hygroscopic behavior and storage stability of acerola pulp powder produced by foam-mat drying. The pulp was foamed with Emustab and dried at 60 °C. Fresh pulp and powder were analyzed for water activity, total titratable acidity, reducing sugars, vitamin C, carotenoids, total phenolic compounds, and antioxidant activity. Powder adsorption isotherms, hygroscopicity, caking, and solubility were evaluated at 10 and 25 °C under relative humidities of 20, 50, 70, and 90%. Drying increased the reducing sugar concentration from 5.24% to 45.49%. The Oswin model best described the Type II sigmoidal isotherms, with R² values of 0.9921 and 0.9974 and mean relative errors of 4.61% and 3.27% at 10 and 25 °C, respectively. Equilibrium moisture content was higher at 25 °C, an atypical behavior associated with the high concentration of low-molecular-weight sugars. Hygroscopicity ranged from 8.77 ± 0.72% to 53.39 ± 0.51%. Although the powder has potential as a functional ingredient, its high hygroscopicity and limited physical stability require moisture-barrier packaging and formulation strategies to improve storage and industrial applicability. Full article

Surface vegetation dynamics in mining areas are characterized by complex non-linear processes associated with anthropogenic disturbance and ecological restoration. Existing remote sensing approaches often face limitations in balancing temporal interpretability and the characterization of long-term vegetation trajectories at regional scales. To address this issue, this study proposes a trajectory-guided weakly supervised framework that integrates parameterized curve fitting with deep temporal learning for mining vegetation monitoring. Based on the characteristic “extraction–reclamation” cycle, six representative vegetation trajectory patterns were pre-defined to describe different stages of degradation and restoration. Long-term NDVI trajectories (1990–2023) derived from Landsat time-series data were modeled using linear and parameterized Sigmoid functions to automatically generate high-quality supervision samples and temporal transition labels. These trajectory-constrained samples were subsequently incorporated into a multi-task BiLSTM-Attention network to simultaneously perform pixel-level change classification and turning-point regression. Applied to the mining clusters of the Dongting Lake Basin, China, the proposed framework achieved an overall classification accuracy of 86.64% (Kappa = 0.83), while the temporal prediction error remained within two years. Results revealed that 28.66% of the 61.20 km² of significantly degraded mining land has undergone effective ecological restoration, with restoration activities increasing sharply between 2012 and 2014 in response to regional environmental policies. By coupling ecological trajectory modeling with weakly supervised temporal learning, this study offers a promising approach for large-scale mining restoration monitoring and ecological assessment. Full article

This paper proposes a hierarchical control architecture that is established at both lower limb joint and platform levels based on a simplified admittance model, where compliance is regulated through virtual mass and damping. At the lower limb joint level, admittance control governs lower limb motion tracking, while at the platform level it adjusts the omnidirectional mobile platform velocity in response to human interaction forces. Within this framework, a Sarsa-based reinforcement learning agent dynamically optimizes the parameters of a Sigmoid function using dual state inputs. Based on hip joint angle error and human–robot interaction force, the controller dynamically adjusts virtual mass and damping to optimize the trade-off between tracking error and dynamic compliance. The simulation and experimental results on the prototype system demonstrate that, compared with traditional Sigmoid parameter-tuned admittance control, the proposed approach significantly enhances gait smoothness (dimensionless squared jerk reduced by 65.62% and 36.74% for hip and knee joints), and increases human–robot interaction compliance (RMS interaction force was reduced from 3.2502 N to 2.5109 N; EPUD decreased from 12.14 to 9.53). Moreover, the proposed strategy achieves smaller maximum overshoot (0.45° vs. 0.9°) and faster settling time (2.6 s vs. 4.59 s). These findings indicate that integrating reinforcement learning with Sigmoid parameter adaptation provides a systematic and effective solution for adaptive compliance regulation in mobile exoskeleton systems, enhancing adaptability, safety, and functional relevance for stroke patients undergoing lower limb rehabilitation training. Full article

Climate change and aging infrastructure are undermining the resilience of urban flood management systems, reducing their reliability and increasing the likelihood of systemic failure that may culminate in regime shifts. This study develops a conceptual and practitioner-oriented screening framework based on a quantitative mathematical model to examine facility-level resilience and identify threshold conditions that may trigger regime transitions under external disturbances and varying pre-disturbance facility conditions. The framework adopts the composite sigmoid function (CSF) to capture nonlinear performance trajectories of infrastructure systems. Building on this model, this study extends its application by developing a parameterization scheme directly linked to four resilience dimensions: robustness, redundancy, rapidity, and resourcefulness (4Rs), which can be derived from field investigations or expert surveys. The normalized 4R scores are mapped to the CSF parameters, thereby converting static resilience assessment results into degradation and recovery curves. To search for threshold conditions, a parametric analysis was conducted by systematically varying the 4R values across their defined ranges. Rather than indicating a single universal threshold value, the results revealed critical threshold regions formed by specific combinations of the 4R dimensions. Lower robustness reduced the initial performance buffer, and low redundancy accelerated and extended performance degradation, while insufficient rapidity and resourcefulness delayed or limited recovery, increasing the likelihood of transition into an alternative degraded regime. For example, even when R1 and R2 were set to relatively high normalized values of 0.90, and R3 was set to its maximum value of 1.00, full recovery could not be achieved when R4 decreased below approximately 0.20. An illustrative application was conducted using preliminary 4R assessment results for flood-control facilities in three districts of Seoul, Korea. The model-derived trajectories were qualitatively compared with reported historical vulnerability patterns. While this comparison was intended as a contextual assessment rather than an event-specific empirical validation, our framework supports comparative, scenario-based screening of potentially vulnerable facilities for preliminary maintenance and investment prioritization. Full article

To addressthe challenge of attitude control in quad-ducted fan unmanned aerial vehicles (UAVs) under coupled disturbances comprising thrust lag, ground effect and a composite wind field during near-ground flight and to mitigate the inherent trade-off between disturbance rejection and noise suppression in fixed-bandwidth extended state observers (ESOs), this paper proposes a robust attitude control method based on a Sigmoid law adaptive-bandwidth extended state observer (AB-ESO). An attitude dynamic model covering the above multi-source disturbances is established, with all uncertainties uniformly treated as lumped disturbances. An adaptive-bandwidth mechanism with filtering and rate-limiting modules is designed for smooth continuous bandwidth tuning. A composite control framework integrating disturbance feedforward, lag compensation and attitude feedback is constructed, and the uniform ultimate boundedness of the closed-loop system is proved. Comparative simulations are conducted against six baseline controllers, including a cascade proportional–integral–derivative (PID) controller, fixed-bandwidth ESOs, incremental nonlinear dynamic inversion (INDI), fast terminal sliding mode control (FTSMC) and a time-varying bandwidth ESO, in a near-ground composite wind scenario. Results show that the proposed method achieves improved comprehensive performance: the three-axis average tracking root mean square error (RMSE) is approximately 72% lower than of the PID controller and 15.8% lower than that of the high-bandwidth ESO, and the control output total variation is reduced by about 27.8%. Monte Carlo verification with 100 random turbulence groups further validates the strong statistical robustness of the proposed method. All validations in this work are based on numerical simulations. This study provides a technical reference for high-precision control of ducted fan UAVs in near-ground environments. Full article

Pyrolysis is a leading route for valorizing lignocellulosic residues, yet detailed multi-step kinetic schemes have so far been deployed only in costly commercial simulators, limiting reproducibility. This work couples thermogravimetric (TGA) characterization with process simulation in the free, open-source simulator DWSIM to predict the pyrolysis product distribution of corn cob from Córdoba, Colombia. The lignocellulosic composition (hemicellulose 24.3 ± 2.9, cellulose 36.4 ± 3.0, lignin 39.3 ± 0.9 wt%) was obtained by deconvolving the derivative thermogravimetric (DTG) curve with a five-parameter asymmetric double sigmoidal (Asym2sig) function (R² > 0.9996). Pseudocomponent activation energies from the Coats–Redfern method (154.2, 124.6, and 29.9 kJ/mol) calibrated the primary reactions of a 17-reaction Ranzi scheme, extended with 18 secondary gas-phase steam reforming reactions. Validated against eight lignocellulosic biomasses, the calibrated model yielded a consolidated R² = 0.853 and average absolute deviation (AAD) = 9.8%, with char predictions most accurate (AAD = 8.9%). For corn cob, a bio-oil-optimized yield of 55.0 wt% was predicted at 500 °C, transitioning to a syngas-rich regime (51.0 wt% gas) at 750 °C. This constitutes the calibrated Ranzi-scheme implementation in DWSIM, offering an accessible, reproducible pathway for biomass pyrolysis modeling. Full article

The purpose of this paper is to derive and propose site-specific joint environmental contours for two eligible Offshore Wind Farm Organized Development Areas (OWFODAs) in the Aegean Sea, Greece. The contours are tailored primarily for the design, structural reliability assessment and definition of site-specific environmental load combinations of offshore wind turbines (OWTs); they are quantified based on publicly available 28-year data sets related to offshore wind and wave conditions, namely, wave height, $H_s$, wave peak period, $T_p$ and mean wind speed at the hub height of the wind turbine, ${\overline{u}}_{hub}$. A new methodology, using the modified Inverse First Order Reliability Method (IFORM), is proposed to accurately reflect the regional climate peculiarities, combined with fifth-order polynomials and a sigmoid function to fit the data of the Weibull parameters and correctly capture the low- and mid-range values of $H_s$, which are statistically far more frequent. Several results, in terms of 2D and 3D contour surfaces for two locations in each OWFODA, for 50-year and 100-year return periods are presented. Finally, two tables are cited: one gathering $H_s$ and $T_p$ values corresponding to the maximum ${\overline{u}}_{\mathrm{h}\mathrm{u}\mathrm{b}}$ conditions, and another gathering ${\overline{u}}_{\mathrm{h}\mathrm{u}\mathrm{b}}$ and $T_p$ values corresponding to the maximum $H_s$ conditions. The presented joint probability distributions and the environmental contour surfaces bridge metocean statistical modelling with renewable energy systems design. By providing site-specific joint metocean conditions, the proposed methodology supports offshore wind farm design and structural assessment, thereby contributing to sustainable wind energy development in the Aegean Sea. Full article

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article

Three-dimensional wind vector retrieval from Coherent Doppler Wind Lidar (CDWL) in Velocity–Azimuth Display (VAD) mode is susceptible to anomalous radial velocity observations induced by low signal-to-noise ratios, clutter echoes, and spectral estimation errors, which degrade inversion accuracy. To address this issue, a robust retrieval method, termed KSR-Huber, is proposed by integrating K-nearest-neighbor (KNN)-based local statistical priors with Huber iterative reweighted least squares (IRLS). The method employs KNN-based local consistency and adaptive Sigmoid weighting, together with Huber residual reweighting within the IRLS framework, to suppress anomalous observations while preserving valid data. Simulations across diverse scenarios, conducted under controlled numerical experiments with varying observation redundancies and outlier contamination levels, show that the proposed method consistently outperforms existing approaches, including DSWF, KNN-COOKS, and airSWF, particularly in terms of robustness under controlled noise and outlier conditions. Real lidar observations further demonstrate the practical applicability of the method, while comprehensive validation against independent reference measurements is left for future work. Full article