Search Results (626)

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article

We examine the growth of Internet Protocol version 6 (IPv6) routing state from 2010 to 2025. The global IPv4 address space has been exhausted, and the transition to IPv6 is ongoing. Using publicly accessible data from the RIPE Route Collectors (RRCs), we show that growth in the number of globally visible IPv6 routing prefixes follows different models over time, reflecting different growth patterns: exponential, power-law, and stretched-exponential. In addition to building models using publicly available RIPE data, we use this data source to demonstrate that our analysis holds across different Internet Exchange Points (IXPs) around the world and has predictive value. We provide in-depth analyses of IPv6 routing state growth, and we believe these are the first such analyses. Additionally, we highlight previous similar analyses of other aspects of network characteristics (such as topology and network traffic), and show that our analyses provide new insights. Specifically, we show the following: (1) previous models that have worked well for other network characteristics do not work well for routing state; (2) growth patterns for IPv6 routing state have changed significantly over time; (3) growth patterns cannot be described by a single model, and need to be analysed in a piecewise fashion; (4) fitting of previous data might not necessarily result in good predictive quality, and we identify the factors that may affect the predictive quality of a model and the predictive models that are suitable at the current time. Our analyses include metrics for assessing model fit. Overall, we observe a decrease in the rate of growth of IPv6 routing state, while the overall use of IPv6 continues to grow. We provide a critical evaluation of our approach, and also discuss possible factors affecting the growth of global IPv6 routing state. Full article

This work presents a spatially explicit 19-variable reaction–diffusion model for within-host SARS-CoV-2 dynamics that integrates viral kinetics, innate and adaptive immune responses, cytokine regulation, antibody production, and tissue-level thermoregulatory dynamics. Adaptive immune recruitment is described through smooth sigmoidal activation functions, whereas pro-inflammatory cytokines are controlled by Michaelis–Menten-type saturation with IL-10 feedback. The thermoregulatory component is formulated as a downstream tissue-level inflammatory readout driven by bounded virus-dependent pyrogenic forcing, homeostatic relaxation, and effective thermal diffusion. The system is solved using a meshless multiquadric radial basis function collocation method based on Kansa’s formulation. Numerical simulations reproduce the qualitative progression of acute infection, including early viral expansion, innate immune activation, delayed adaptive recruitment, and immune-mediated clearance. Spatial analysis reveals heterogeneous tissue-level patterns, such as localized viral foci, antibody depletion near the infection center, delayed cytotoxic effector coverage, and transient thermal gradients. The proposed framework provides a biologically interpretable and computationally flexible approach for investigating the spatiotemporal organization of within-host SARS-CoV-2 immune dynamics, while remaining a mechanistic modeling study rather than a patient-specific clinical predictor. Full article

Urban forests are widely promoted for improving air quality, yet their effectiveness is typically assessed through static green-space indicators that ignore seasonal variation in vegetation activity. This limitation is especially consequential in cold-region cities, where winter heating-season pollution peaks coincide with the leaf-off period of deciduous trees. Using a monthly panel of 15 centrally heated cities in northern China (2015–2024; N = 1464), this study develops a phenology-aware framework integrating three indicators: effective forest capacity (EFC), which combines dynamic forest area with a sigmoid leaf-on share and city-specific evergreen fraction; heating-season exposure (HI); and a binary phenology–heating mismatch (PHM) flag. City–year–month fixed-effects models show that the EFC–PM_2.5 association is directionally negative but statistically inconclusive under conservative inference (city-clustered SE: $p=0.523$; wild bootstrap: $p=0.541$), whereas associations with SO₂ and O₃ are statistically robust. The central empirical contributions are the four-quadrant heterogeneity analysis and the topographic paired comparison: four-quadrant heterogeneity analysis reveals that forest capacity shows clearer negative associations in dry semi-humid cities, whereas HI dominates in heating-dominated plain cities. A paired topographic comparison between Urumqi and Xining illustrates how terrain-induced inversions can override forest signals. The results support differentiated urban greening strategies that coordinate forest expansion with heating-system transition, evergreen species planning, and ventilation-sensitive urban design. Full article

This paper presents a mechanistic model for a thin-layer microalgal bioreactor cultivating Stigeoclonium nanum, with a comprehensive analysis of its dynamics and stability. Unlike most bioreactor studies that assume simple Monod or linear growth, our model rigorously explores the nonlinear interplay between logistic constraints and multiple nutrient limitations. We introduce a coupled Logistic–Monod system of nonlinear ordinary differential equations that captures sigmoidal transitions and steady states of Stigeoclonium nanum under simultaneous nitrogen and phosphorus depletion and incorporates abiotic nutrient removal to ensure mass conservation. Qualitative analysis proves positive invariance and boundedness of solutions using the Localization of Compact Invariant Sets method. Asymptotic stability of the biologically relevant equilibrium is established. Experimental validation in a thin-layer photobioreactor using three-fold cross-validation yielded high correlation coefficients (0.78–0.96) for biomass, nitrate, and phosphate concentrations, confirming predictive accuracy. The model thus provides a robust framework for process control and optimization in industrial-scale applications. Full article

Wire drawing is a key processing method for producing ultrahigh-strength stainless steel wires. In metastable austenitic steels, the strain-induced martensitic transformation is known to govern strain hardening. However, the transformation mechanism and kinetics behavior under wire drawing remain unclear due to the distinct deformation conditions compared to those of conventional loading modes. In this work, the microstructural evolution, transformation kinetics and strengthening behavior of the 304 stainless steel during cold wire drawing are systematically analyzed. The results show that the transformation is dominated by the austenite → twin→ α′-martensite pathway, with the ε-martensite effectively suppressed. The martensite fraction follows a sigmoidal evolution with the equivalent drawing strain and could be well described by the Olson–Cohen model. The yield strength is increased from 320 MPa to 2 GPa and exhibits a linear relationship with the martensite fraction, indicating a dominant composite strengthening mechanism. These findings clarify the deformation-mode-dependent transformation mechanism and its role in governing mechanical properties during wire drawing. Full article

Lift-plus-cruise electric vertical takeoff and landing (eVTOL) aircraft exhibit complex stability characteristics during transition flight, when rotor-borne and wing-borne regimes coexist. This work investigates the dynamic stability of a lift-plus-cruise eVTOL using a nonlinear six-degree-of-freedom model incorporating aerodynamic forces, tractor propulsion, and vertical lifter dynamics. Linearization about representative trimmed conditions enables longitudinal and lateral–directional modal analysis. The results identify a critical near-stall region where lift-curve slope reduction markedly decreases short-period damping. Residual lifter authority partially compensates for this degradation, improving stability in the transition regime. To ensure smooth control transfer, an airspeed-dependent blending strategy between hover and fixed-wing controllers is implemented. Comparative analyses show that a sigmoid blending law improves the minimum short-period damping ratio relative to a linear strategy while preserving similar overall damping variation. Closed-loop simulations of a complete mission profile demonstrate the effectiveness of the proposed approach and reveal an asymmetric dynamic response between hover-to-forward and forward-to-hover transitions. These findings provide a physically grounded explanation for stability degradation during transition and establish practical guidelines for control authority blending in lift-plus-cruise eVTOL aircraft. Full article

An artificial neural network (ANN) prediction model based on the Levenberg–Marquardt (LM) algorithm has been developed to predict the nonlinear heat and mass transfer characteristics of Cattaneo–Christov Carreau–Yasuda tri-hybrid nanofluid (CCHMF–THNF) flow over a porous stretching sheet. A mathematical model of the phenomenon was developed based on a number of elements, including the combined effect of magnetohydrodynamic forces, thermal and solutal relaxation and the influence of viscoelastic fluid behavior and is numerically analyzed utilizing MATLAB bvp4c software. A set of standard data was generated as a reference for developing the ANN-LM model with one hidden layer containing 10 neurons and log-sigmoid activation function, to achieve rapid predictions of velocity, temperature and concentration profiles from the identified data set. This study introduces a novel methodology to provide fast prediction capabilities for transport characteristics through integration of the ANN–LM model with the non-linear CCHMF-THNF model, producing computational savings by providing prediction accuracy of transport characteristics with MSE values on the order of $1.0\times {10}^{-10}$ using ANN–LM in place of repeated bvp4c solutions. Furthermore, the predictive capability of the developed ANN–LM framework may be beneficial in the areas of thermal management systems, polymer processing, energy transport applications, and magnetically controlled cooling technologies since they all share a need for fast access to transportation characteristic evaluation data. Full article

The circular valorization of biomass for sustainable energy recovery is a strategic priority in the transition toward low-carbon systems. In the last decade, anaerobic digestion (AD) has emerged as an efficient technology to produce an energetic vector to replace natural gas with biomethane and reduce waste; however, the hydrolysis of refractory fractions remains the main rate-limiting step. This study investigates an innovative electro-assisted pretreatment of biomass to promote the first rate-limiting hydrolysis step of refractory compounds in biomethane production. Lignocellulosic residues are employed not only as feedstock for the AD process but also as substrates in electrohydrolysis (EH) pretreatment using an Ir-Ta mixed metal oxide (MMO) anode coupled with advanced biomass-derived carbon felt cathodes. Two cathodes were functionalized with Phragmites Australis (PhA) hydrochars, untreated (PA) and KOH-activated (PA-KOH), to enhance the in situ generation of reactive oxygen species (ROS). Brassica napus (Bn) was chosen as the other biomass selected as a feedstock of AD, and was subjected to EH at varying energy inputs (500–5000 kJ kg⁻¹), evaluating structural and biochemical shifts. The results demonstrate that EH effectively modifies the biomass matrix; the PA-KOH-CF cathode exhibited good selectivity to degrade lignocellulosic structures, but higher biomethane production was achieved at 2500 kJ·kg⁻¹ TS using PA-CF, reaching an increase of 52% compared with untreated samples. Kinetic analysis of the biomethane potential was performed using the modified Gompertz model. The model accurately captured the asymmetric sigmoidal transitions of methane production with different electrode configurations, and finally, energy balance assessment identified 2500 kJ·kg⁻¹ TS as the optimal operational threshold. These findings suggest that an excess of applied energy is critical to the availability of soluble organic matter and the presence of refractory compounds that reduce efficiency. This electro-assisted approach offers a robust strategy for intensifying AD, aligning with circular bioenergy objectives. Full article

Explainable artificial intelligence is increasingly needed in high-stakes tabular classification, where predictions should be accurate, auditable, and easy to inspect. We propose GRS-ANFIS, a role-separated neuro-fuzzy model that decomposes inference into a Primary module for main decision formation and a Complementary module for targeted correction. During differentiable training, sigmoid gate values are applied only to consequent coefficients, while the antecedent part receives the original input without soft masking. After each stage, the learned gates are binarized into hard routing masks that define discrete antecedent and consequent subsets for module-specific fine-tuning. The Complementary module is restricted to variables not selected by the Primary module, yielding explicit role separation and disjoint variable usage across modules. To support stable learning in high-dimensional settings, all ANFIS-family models use the same HTSK-style firing computation. Experiments on four tabular benchmarks show that GRS-ANFIS achieves competitive predictive performance while maintaining compact, role-separated rule structures; rule-count compactness is clear, whereas the unified Nauck/HFSi interpretability values are dataset- and variant-dependent. Boundary-focused analysis further shows that the Complementary module mainly improves difficult, low-confidence samples through targeted correction. Full article

Gesture recognition based on surface electromyography (sEMG) signals typically involves extracting features from the signals and then incorporating recognition models to increase the accuracy of classification. In this paper, drawing on the properties of sEMG signals and statistical principles, we propose a novel feature extraction method called “multiple mapping”, designed to construct a high-performance representation of sEMG signals. The multiple mapping approach incorporates sequential mappings, including the sliding average power, lg (base-10 logarithm) mapping, a linear compression principle, and a sigmoid normalization function. The sliding average power captures the intensity variations in sEMG signals across various channels, allowing differentiation between the activity levels of distinct muscle groups. lg mapping adjusts the distribution of the sEMG signals to improve their uniformity, enhancing feature stability and facilitating comparisons. The linear compression and sigmoid normalization emphasize the signals’ central characteristics while compressing the extremes at both ends. Then, the sEMG signals obtained from multiple mapping are transformed into Sem grayscale maps, which are subsequently processed using the deep neural network ResNet50 for gesture recognition. Extensive experiments on three public datasets were conducted, and the average recognition accuracy was 95.26%, 90.81%, and 96.72%, respectively, while it was 96.8% in self-collected recognition tasks. The results demonstrate that the multiple mapping method significantly improves the feature extraction performance for sEMG-based gesture recognition, offering a promising direction for applications in prosthetic gesture control and muscle–computer interaction. Full article

The response of tree growth to environmental (climatic) changes has largely been analyzed through ring width–climate relationships, yet such analyses often lack the dynamic process of radial growth in response to environmental changes. Therefore, this study focuses on Korean pine (Pinus koraiensis Siebold & Zucc.) and white birch (Betula platyphylla Sukaczev) at three elevations (750 m, 950 m, and 1150 m) in the broadleaved Korean pine forest on the northern slope of Changbai Mountains, China. We systematically monitored cambial activity and the dynamics of xylem formation stages to analyze the different adaptation strategies of the two species in terms of phenology, cellular characteristics, growth rates, and climatic responses during cambial and xylem formation stages. The results showed that the phenological stages of xylem formation in Korean pine were more sensitive to elevation, while the phenological changes in birch were smaller, indicating greater growth stability. The seasonal dynamics of the number of xylem cell layers in both species followed a unimodal or sigmoid curve, but high elevations significantly inhibited the number of mature cell layers. Gompertz model fitting revealed that the maximum growth rate of Korean pine decreased significantly with increasing elevation, whereas no significant change was observed in birch. With increasing elevation, temperature emerged as the primary factor influencing cambial phenology and growth duration in both species, while precipitation dominated changes in growth rates. Xylem growth in Korean pine was co-regulated by growth rate (R² = 0.62) and growth duration (R² = 0.35), with tracheid diameter closely related to the duration of expansion (R² = 0.36). The regulatory pattern of xylem growth in birch was similar to that in Korean pine but with weaker correlations. In summary, Korean pine, as a coniferous dominant species, is more sensitive to temperature changes induced by elevation and adapts to elevational variations by adjusting phenology and cell development. In contrast, birch, as a broadleaved pioneer species, exhibits a high buffering capacity in xylem formation in response to elevational changes, thereby maintaining growth stability. The divergent growth strategies of the two species reveal the potential response pathways of temperate forest tree species to environmental changes and provide important insights for predicting the dynamics of broadleaved Korean pine forests. Full article

This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves $\epsilon$-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of $\eta \le 0.12$ under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of $\lambda \ge 0.23$; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation ($q_{comm}<0.3$). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions ($R^2=0.89$, $p<0.001$), supporting the theoretical model with empirical evidence. Full article

Background/Objectives: Hungry bone syndrome (HBS) is a frequent and potentially life-threatening complication following parathyroidectomy (PTX) for secondary hyperparathyroidism (SHPT) in dialysis patients, yet existing prediction tools offer limited discriminative accuracy. This study aimed to develop and internally validate an interpretable machine learning (ML) framework for preoperative HBS prediction and to derive a pragmatic bedside risk score from ML-derived feature importance. Methods: Ninety end-stage renal disease patients who underwent PTX for drug-refractory SHPT at a single center (2019–2023) were analyzed. Eight supervised ML classifiers were trained on 24 preoperative features (19 raw variables plus 5 engineered features) and evaluated under 5-fold stratified cross-validation repeated 10 times. SHapley Additive exPlanations (SHAP) analysis was applied for model interpretability, and a composite bedside risk score was constructed from SHAP-derived feature rankings. Results: HBS occurred in 41 patients (45.6%). Random forest achieved the numerically highest discrimination among multi-feature models (AUC = 0.933 ± 0.065), outperforming previously published models, though univariate alkaline phosphatase (ALP) alone achieved a comparable cross-validated AUC of 0.958. ALP overwhelmingly dominated all predictors (mean |SHAP| = 3.37, exceeding the next-ranked feature by approximately 6.5-fold). Partial dependence analysis revealed a sigmoid-shaped ALP–HBS relationship with a critical inflection zone between 250–350 U/L, and SHAP dependence plots demonstrated that total parathyroidectomy amplifies ALP-mediated risk. A SHAP-guided composite bedside risk score (range 0–9) achieved an AUC of 0.883, with observed HBS rates rising monotonically from 0% (score 0) to 100% (score ≥ 6). Decision-curve analysis showed that univariate ALP and the multi-feature pipeline yielded comparable net benefit, with ALP preferable in the high-sensitivity regime and the multi-feature model preferable at high-specificity thresholds; net reclassification improvement was negative for the multi-feature model vs. univariate ALP, supporting the framework’s role as an interpretive rather than discriminative advance. Conclusions: An interpretable ML framework substantially improves HBS prediction over conventional models, confirms ALP as the overwhelmingly dominant predictor through a nonlinear dose–response relationship, and yields a clinically interpretable bedside risk score that, pending external validation, may support preoperative risk stratification. Full article

Mass trapping of adult mosquitoes is increasingly promoted as an environmentally friendly alternative to insecticide-based vector control, yet quantitative evidence for its long-term population-level effects remains limited. We analyzed adult Aedes mosquito Biogents trap data from four tropical islands (three in the Maldives, one in the Philippines) where mass trapping was implemented at different trap densities. Using equilibrium-constrained population models, we describe how adult Aedes populations differ across trap densities, with outcomes ranging from partial suppression to near-zero levels at higher trap densities. At low to intermediate densities (4–6 traps·ha⁻¹), populations stabilized at non-zero equilibrium levels, whereas operational elimination was consistently observed at densities ≥ 10 traps ha⁻¹. A descriptive curve is shown to illustrate the decline in equilibrium abundance with increasing trap density, while a conceptual sigmoid model is used to illustrate how a transition in the recruitment–removal balance may occur under theoretical conditions. Limited larval source management was implemented on two islands, but elimination was also observed in the absence of larval interventions, indicating that sustained adult removal appears to have been the dominant driver of suppression. These findings indicate that mass trapping, when deployed at sufficiently high densities, is associated with rapid declines to near-zero population levels and may serve as an effective component of integrated vector management, particularly in geographically bounded settings or as a rapid-response intervention during outbreaks of arboviral diseases. Full article