Search Results (4,061)

Multimessenger constraints tightly bound the gravitational-wave speed to be luminal, posing a strong filter for modified gravity. This paper develops a symmetry-selected Palatini framework with torsion in which exact luminality at quadratic order is achieved by construction rather than by parameter tuning. Two ingredients shape the observable sector: (i) a scalar PT projector that keeps scalar densities real and parity-even, and (ii) projective invariance implemented via a non-dynamical Stueckelberg compensator that enters only through its gradient. Under an explicit posture (A1–A6), we establish three structural results: (C1) algebraic uniqueness of torsion to a pure-trace form aligned with the compensator gradient; (C2) bulk equivalence, modulo improvements, among a rank-one determinant route, a closed-metric deformation, and a PT-even CS/Nieh–Yan route; and (C3) a coefficient-locking identity that enforces $K=G$ for tensor modes on admissible domains; hence, $c_T=1$ with two propagating polarizations. Beyond leading order, the framework yields a distinctive, falsifiable next-to-leading correction $\delta c_T^2\left(k\right)=b{k}^2/{\Lambda }^2$ (for $k\ll \Lambda$), predicting slope 2 in log–log fits across frequency bands (PTA/LISA/LVK). The analysis is formulated to be reproducible, with a public repository providing figure generators, coefficients, and tests that directly validate (C1)–(C3). Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Fermented forest litter (FFL) is a biofertilizer obtained by anaerobic fermentation of forest litter combined with agricultural by-products. Its production involves an initial one-month solid-state fermentation of oak litter mixed with whey, molasses and wheat bran, followed by a one-week submerged fermentation-called the “activation” phase-during which the solid FFL is fermented with sugarcane molasses diluted in water. This study aimed to evaluate the effects storage duration (6, 18 and 30 months), and temperature (ambient and 29 °C) on the activation phase. For this purpose, pH, sugar consumption and metabolite production dynamics were monitored. Under all experimental conditions, the pH dropped to values close to 3.5, sucrose was rapidly hydrolyzed, and glucose was preferentially consumed over fructose. Fructose was metabolized only after glucose was depleted, suggesting the involvement of fructophilic microorganisms. The time-course evolution of lactic acid (LA) concentration was adequately fitted by the Gompertz model (R² > 0.970). The highest LA_max concentration (6.30 g/L) and production rate (2.16 g/L·d) were obtained with FFL stored for 6 months. Acetic acid (AA) and ethanol were also detected reaching maxima values of 1.19 g/L and 0.96 g/L, respectively. Their profiles varied depending on the experimental conditions. Notably, the AA/LA ratio increased with the age of the FFL. Overall, sugar consumption and metabolite production were significantly slower at ambient temperature, than at 29 °C. These results contribute to a better understanding of the metabolic dynamics during FFL activation and highlight key parameters that should be considered to optimize future biofertilizer production processes. Full article

The adsorption of methylene blue (MB) on poly(vinylidene fluoride) (PVDF) and PVDF/titanium dioxide(TiO₂) membranes with 1.5 wt% dosage was examined through batch adsorption and dynamic cross-flow filtration experiments. The effects of pH, temperature, and initial MB concentration on adsorption performance were evaluated via batch experiments. The Thomas model was applied to analyze the membrane filtration process, while kinetic, isothermal, and thermodynamic models were integrated to elucidate the adsorption mechanisms. Results demonstrated that low temperature and high initial MB concentration significantly improved MB adsorption on both membranes. Under neutral pH conditions (pH = 7), the maximum adsorption capacities of PVDF and PVDF/TiO₂ membranes reached 1.518 ± 0.025 mg/g and 0.189 ± 0.008 mg/g, respectively. The adsorption processes on both membranes conformed to the pseudo-second-order kinetic model, with optimal fitting to the Langmuir isotherm model. Thermodynamic analysis revealed physical adsorption mechanisms, as evidenced by adsorption free energy (E) calculated via the Dubinin–Radushrevich model Notably, PVDF membrane exhibited a more pronounced mass transfer zone height (h_Z = 2.3 ± 0.1 cm) and achieved higher adsorption capacity (2.1 ± 0.09 mg/g) than PVDF/TiO₂ membranes (0.25 ± 0.01 mg/g). The TiO₂ incorporation reduced hybrid membrane adsorption capacity and significantly mitigated membrane fouling caused by adsorption, with PVDF/TiO₂ membranes showing a 32 ± 2.5% lower flux decline rate than PVDF membranes with less MB into the pores. This study provides fundamental data supporting the combined application of “adsorption–subsequent oxidation” using PVDF-based membranes in dye wastewater treatment. Full article

Mathematical models for infectious diseases, particularly autonomous ODE models, are generally known to possess simple dynamics, often converging to stable disease-free or endemic equilibria. This paper investigates the dynamic consequences of a crucial, yet often overlooked, component of pandemic response: the saturation of public health testing. We extend the standard SIR model to include compartments for ‘Confirmed’ (C) and ‘Monitored’ (M) individuals, resulting in a new SICMR model. By fitting the model to U.S. COVID-19 pandemic data (specifically the Omicron wave of late 2021), we demonstrate that capacity constraints in testing destabilize the testing-free endemic equilibrium ($E_1$). This equilibrium becomes an unstable saddle-focus. The instability is driven by a sociological feedback loop, where the rise in confirmed cases drive testing effort, modeled by a nonlinear Holling Type II functional response. We explicitly verify that the eigenvalues for the best-fit model satisfy the Shilnikov condition (${\lambda }_u>{\lambda }_s$), demonstrating the system possesses the necessary ingredients for complex, chaotic-like dynamics. Furthermore, we employ Stochastic Differential Equations (SDEs) to show that intrinsic noise interacts with this instability to generate ’noise-induced bursting,’ replicating the complex wave-like patterns observed in empirical data. Our results suggest that public health interventions, such as testing, are not merely passive controls but active dynamical variables that can fundamentally alter the qualitative stability of an epidemic. Full article

Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (W_d) and fresh weight (W_f), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and W_f, as well as between W_d and W_f, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R² > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models. Full article

Background/Objectives: Precise implant positioning is critical for successful prosthetic rehabilitation, particularly in completely edentulous patients where anatomical landmarks are lost. The aim of this study was to assess the accuracy of implant placement in the edentulous maxilla and mandible using computer-assisted planning and a bone-supported stackable surgical guide protocol. Methods: This retrospective clinical study included 15 completely edentulous patients who received a total of 60 implants. A dual-scan protocol was utilized for planning. The surgical protocol involved a base guide fixed to the bone with pins, serving as a rigid foundation for stackable components used for osteotomy and implant insertion. Postoperative CBCT scans were superimposed onto the preoperative plan to calculate angular deviations, 3D linear deviations at the implant neck and apex, and depth deviations. Results: The analysis demonstrated high accuracy with a mean angular deviation of 1.25° ± 0.80°. The mean 3D linear deviation was 0.96 ± 0.57 mm at the implant neck and 1.07 ± 0.56 mm at the apex. Depth deviation showed a mean discrepancy of 0.37 ± 0.58 mm. All measured parameters were statistically significantly lower (p < 0.05) than the pre-established clinical safety thresholds. Conclusions: Within the limitations of this study, the bone-supported stackable surgical guide protocol proved to be a highly accurate method for full-arch rehabilitation. By eliminating mucosal resilience and ensuring rigid fixation, this approach enables predictable implant placement and facilitates the passive fit of screw-retained bar-supported prostheses, representing a reliable alternative to dynamic navigation in daily clinical practice. Full article

A fixed-time sliding mode controller based on an adaptive neural network is developed for the path tracking problem of robotic manipulators with model uncertainty and external nonlinear interference. Firstly, a fixed-time sliding surface and sliding mode reaching law are designed based on the dynamic model of the robotic manipulator, which ensures that the error signal converges along the sliding surface within a fixed time. The speed of the state approaching the sliding surface can be flexibly adjusted through the reaching law, and it has strong robustness to parameter perturbations and external disturbances. Then, the uncertainty of model parameters and external disturbances is regarded as composite interference, and an adaptive neural network is utilized to approximate the disturbance online for adaptive fitting. This does not require precise modelling, the control input jitter is reduced, the composite disturbance is compensated in real time, and the system tracking accuracy is improved. Subsequently, the fixed-time stability characteristics of the closed-loop system are demonstrated through Lyapunov stability theory. Finally, the effectiveness and robustness of the proposed control strategy are verified through simulation. Full article

This study examines the phenomenon of “foolishness for Christ” as reflected in the contemporary Orthodox figure of Elder Gerontius of Tismana. Starting with a general review of the diverse phenomena of divine madness present in various world religions, we then move onto the Orthodox Christian tradition, where such apparent eccentric behavior is interpreted as an expression of deep asceticism and spiritual insight. Based primarily on memorial and testimonial sources (oral accounts, written recollections, and biographical notes), the research employs a hermeneutical and phenomenological approach to interpret how such figures are perceived within ecclesial life. Using Christos Yannaras’ theological criteria for discerning authentic “holy folly”, our paper argues that Elder Gerontius convincingly fits this ascetic paradigm. The study further suggests that the presence of such charismatic and unconventional figures may signal a form of spiritual renewal within contemporary Orthodoxy, revealing the dynamic interplay between prophetic charisma and institutional order in the life of the Church. Full article

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article

Aging induces sarcopenia and reduces bone mineral density, altering body composition. These modifications contribute to physical decline, increase non-communicable disease risk and increase the likelihood of hospitalization, thereby representing a substantial public health burden. In this study, we assessed the effects of isometric training with neuromuscular electrical stimulation conditioning contractions (ISO-NMES) and dynamic resistance training (DRT) on physical and functional capacities. Moreover, we investigated the impact of ISO-NMES training on the force and power of the trained and untrained leg. Eighteen sessions of ISO-NMES training for knee extensors were performed by 10 older adults (age: 70.1 ± 4.9 years; ISO-NMES group). The DRT group (n = 12; age: 70.5 ± 2.8 years) performed 18 sessions of dynamic resistance training at a local fitness center. Maximum voluntary contraction (MVC) and peak power (P) of lower limbs as well as functional capacities assessed with the 5 Sit to Stand, Timed Up and Go and 6 Minutes Walking Tests were examined in both groups before and after the related training protocols. At the end of the training period, only the ISO-NMES group had improved MVC (+30.4%, p < 0.001) and bilateral force (ISO-NMES: +6.3%, p = 0.032). Moreover, both groups had significantly improved functional capacities. Finally, in the ISO-NMES group, MVC, force and power significantly increased in both legs with a greater effect for MVC in the trained than untrained leg (+30.4 vs. +13.5%, p < 0.001). These findings suggest that ISO-NMES training was an effective strategy to improve physical and functional capacities in older adults. Thus, it could be considered as a potential intervention, particularly when the mobility to perform physical training is limited. Full article

Pest detection in the field is crucial for realizing smart agriculture. Deep learning-based target detection algorithms have become an important pest identification method due to their high detection accuracy, but the existing methods still suffer from misdetection and omission when detecting small-targeted pests and small-targeted pests in more complex backgrounds. For this reason, this study improves on YOLO11 and proposes a new model called MSDS-YOLO for enhanced detection of small-target pests. First, a new dynamic multi-scale feature extraction module (C3k2_DMSFE) is introduced, which can be adaptively adjusted according to different input features and thus effectively capture multi-scale and diverse feature information. Next, a novel Dimensional Selective Feature Pyramid Network (DSFPN) is proposed, which employs adaptive feature selection and multi-dimensional fusion mechanisms to enhance small-target saliency. Finally, the ability to fit small targets was enhanced by adding 160 × 160 detection heads removing 20 × 20 detection heads and using Normalized Gaussian Wasserstein Distance (NWD) combined with CIoU as a position loss function to measure the prediction error. In addition, a real small-target pest dataset, Cottonpest2, is constructed for validating the proposed model. The experimental results showed that a mAP50 of 86.7% was achieved on the self-constructed dataset Cottonpest2, which was improved by 3.0% compared to the baseline. At the same time, MSDS-YOLO has achieved better detection accuracy than other YOLO models on public datasets. Model evaluation on these three datasets shows that the MSDS-YOLO model has excellent robustness and model generalization ability. Full article

The trend of the world to electric mobility and the inclusion of renewable energy requires complex control and predictive models of Smart Electric Vehicle Charging Stations (SEVCSs). The paper describes an experimental artificial intelligence (AI) model that can be used to optimize EV charging in New York City based on ten years of historical load and weather information. Nonlinear environmental relationships with urban energy demand and the use of Neural Fitting and Regression Learner models in MATLAB were used to explore the nonlinear relationships between the environment and energy demand. The quality of the input data was maintained with a lot of preprocessing, such as outlier removal, smoothing, and time alignment. The performance measurements showed that there was a Mean Absolute Percentage Error (MAPE) of 4.9, and a coefficient of determination (R²) of 0.93, meaning that there was a high level of concordance between the predicted and measured load profiles. Such findings indicate that AI-based models can be used to replicate load dynamics during renewable energy variability. The research combines the findings of long-term and multi-source data with the short-term forecasting to address the research gaps of past studies that were limited to a few small datasets or single-variable-based time series, which will provide a replicable base to develop energy-efficient and intelligent EV charging networks in line with future grid decarbonization goals. The proposed neural network had an R² = 0.93 and RMSE = 36.4 MW. The Neural Fitting model led to less RMSE than linear regression and lower MAPE than the persistence method by a factor of about 15 and 22 percent, respectively. Full article

Modern processes to produce rare-earth elements, strategic metals, and nuclear fuel reprocessing require highly efficient liquid–liquid extraction in systems characterized by high viscosity, elevated interfacial tension, and small density differences. Traditional gravity-driven extractors exhibit low performance under these conditions, whereas centrifugal extractors enable rapid mass transfer and nearly complete phase separation. Differential-contact annular centrifugal contactors offer the highest flexibility and efficiency, but their optimization is limited by the lack of experimental data on the hydrodynamics of liquid flow through perforated nozzles in a rotating field. This limitation hinders the development of accurate computational fluid dynamics (CFD) models (e.g., ANSYS Fluent), reliable equipment scale-up, and the design of optimized contactor configurations. The present study addresses this gap by experimentally determining the flow velocity of liquids through nozzles of various geometries across a wide range of centrifugal accelerations. From these data, a universal power-law correlation was derived, linking the flow rate to rotor speed, nozzle geometry, and the physicochemical properties of the phases. The proposed correlation provides a robust experimental basis for numerical model validation, computational design, and optimization of next-generation differential-contact centrifugal extractors. Full article

Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety. This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab’s X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems. Full article