Search Results (778)

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination ($R_{adj}^2$) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

Accurate performance prediction of variable-speed centrifugal chillers is important for building energy optimization and the development of digital twins in HVAC systems. In practice, obtaining extensive operational data is costly, creating a prevalent “small-sample” dilemma under which conventional data-driven models are prone to overfitting with poor extrapolation capability. While recent Physics-Informed Neural Networks (PINNs) incorporate system-level thermodynamic constraints (e.g., COP definitions), they typically treat the centrifugal compressor as a thermodynamic black box, neglecting its inherent fluid dynamic characteristics; consequently, extrapolated predictions may be physically inconsistent or fall into unsafe operating regions such as compressor surge. To address this gap, this paper proposes an Aero-thermodynamic Physics-Informed Neural Network (Aero-PINN) that introduces three mechanisms into the PINN loss function: (1) dimensionless aerodynamic similarity mapping governed by affinity laws, (2) a surge boundary constraint that prevents non-physical extrapolations, and (3) an aerodynamic–electrical energy coupling validation. Experimental validation on 420 real-world variable-speed test records shows that the Aero-PINN achieves a COP RMSE of 0.04 and a COP MAPE of 0.3%, outperforming standard MLP and polynomial baselines. Moreover, 100% of the extrapolated operating points satisfy all fluid dynamic safety and energy efficiency constraints. This framework provides a reliable, physics-constrained small-sample learning approach, facilitating factory calibration and reduced-test digital modeling for chiller plants. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article

This paper proposes a temperature measurement and system identification method for confined cavity explosions based on an improved type C thermocouple sensor. On the one hand, to address the extreme conditions caused by high-speed fragments and intense shock waves in an enclosed explosive environment, a thermocouple probe structure employing alloy strips of different widths with an alumina insulating layer in between is designed. By optimizing the strip width, the contact issues arising from edge-cutting burrs are effectively suppressed, thereby significantly enhancing the electrical insulation performance and overall reliability of the sensor. Additionally, a wedge-shaped alumina ceramic piece is designed to secure the thermocouple probe, further improving its structural stability under impact conditions. On the other hand, to tackle the highly nonlinear and multi-field coupled characteristics of the post-explosion temperature field, a system identification method based on the least square method is proposed. This method constructs a polynomial function in terms of radial distance and time variables, enabling effective reconstruction of the temperature field from limited measurement points. It provides a useful reference for understanding of the temperature distribution in confined cavity explosions and supports improved estimation of the temperature field. Finally, experimental results demonstrate that the improved sensor exhibits good survivability and measurement reliability under extreme explosive conditions. Meanwhile, the reconstructed temperature field model shows high fitting accuracy and good capability for describing the temperature distribution, confirming the effectiveness of the proposed identification method. Full article

Time delays are inherent in modern motion-control and electric-drive loops due to sensing, filtering, sampling and computation, communication, and actuation scheduling. When such delays are only partially known, they can markedly reduce stability margins and narrow the admissible range of state-feedback gains, especially in high-bandwidth servo applications. This paper develops a design-oriented state-feedback framework for delay-affected plants based on the Manabe polynomial concept and the Coefficient Diagram Method (CDM). The plant is represented as a chain of integrators of order two to four with an effective input gain, and the feedback gain is synthesized for the nominal delay-free model by matching a standard Manabe/CDM characteristic polynomial using the classical CDM stability-index pattern. When an unmodeled input delay is present, the closed loop is governed by a delay-dependent characteristic equation. By introducing a normalized representation, the analysis yields explicit delay-stability limits that directly translate into a lower bound on the equivalent time constant used for tuning. The degradation of the phase margin and gain margin with increasing normalized delay is quantified as design charts, and a simple phase-margin-based inequality is proposed for selecting the tuning time constant, with gain-margin checks recommended as a verification step. Full article

This population-based study aimed to analyze the annual incidence and case fatality trends, and the clinical-epidemiological profile of gastroschisis in the state of Paraná, Brazil, between 2013 and 2024. Specifically, temporal trends in annual incidence and mortality rates related to gastroschisis were examined. Maternal, gestational, and neonatal characteristics were analyzed. Data from the Live Birth Information System and the Mortality Information System were analyzed using polynomial regression modeling. During the study period, 1,798,727 live births were recorded, including 491 cases of gastroschisis and 179 related deaths. The mean incidence was 2.73 per 10,000 live births. A significant 39.5% decrease over the study period was observed (p < 0.001). The case fatality rate was 36.5%. The mothers of children with gastroschisis were: young mothers (<25 years old; 77%), with low education (87.7%) and no partner (59.1%). High frequencies of cesarean deliveries (84.3%), prematurity (57.3%), low birth weight (63.7%), and low Apgar scores were also observed. The profiles of the mothers and children at birth were unfavorable when compared to the population of live births. Gastroschisis incidence in Paraná declined significantly from 2013 to 2024. While the annual incidence showed a decreasing trend, mortality fluctuated. The persistently high case fatality rate underscores the need for public policies focused on prenatal care and specialized neonatal management. Full article

This study investigated the potential of black soldier fly larvae (BSFL) meal to replace soybean meal in broiler diets by evaluating growth performance, carcass traits, meat quality, and blood biochemical responses. A total of 160 ten-day old Ross 708 chicks (216.74 ± 0.74, g) were randomly assigned to four dietary treatments containing 0%, 20%, 40%, or 60% BSFL meal replacing soybean meal on a 100% equivalent basis, respectively, and evaluated during the starter (10–21 days), grower (21–42 days), and overall (10–42 days) phases. Carcass characteristics, meat color, and blood biochemistry were assessed on day 42. Data was analyzed using polynomial (linear and quadratic) contrasts. Increasing dietary BSFL levels resulted in significant reductions in body weight, average daily gain, and feed intake, while the feed conversion ratio increased linearly. Carcass yield decreased to higher inclusion levels, accompanied by a marked increase in gizzard weight. Meat color (L*, a*, b*) remained largely unchanged across treatments. Blood biochemical analysis revealed linear and quadratic shifts in key metabolites, enzymes, and electrolytes, including reductions in aspartate aminotransferase, bilirubin, and creatine phosphokinase, as well as altered calcium and phosphorus concentrations. Overall, BSFL meal inclusion as 20% replacement improved growth performance and stimulated beneficial lipid and protein metabolism adaptations in broilers. Full article

Rigid polyurethane foams are often manufactured in sealed molds, so knowledge of the density distribution in the molded blocks is essential. A study was conducted with the aim to estimate density distribution within a rigid polyurethane foam block (average core density of ≈96 kg/m³) manufactured in a rectangular sealed mold. The density of 150 rectangular samples was determined experimentally. Characteristic locations of the foams’ columns in the block were outlined, having similar foaming conditions. Averaged density in the characteristic columns was calculated for each characteristic location. A mathematical model was developed based on density data of characteristic columns, approximated with second- and third-degree polynomials. Density distribution was calculated, and corresponding color charts with density zones and equidensity lines were constructed for six horizontal and two vertical sections of the block. It was found that the common center of the elliptical equidensity lines is located asymmetrically, ≈17 mm above the geometric center of the untrimmed block. Density gradients were calculated in directions parallel and perpendicular to the foams’ rise direction. The developed mathematical model allowed us to estimate density distribution within the rigid polyurethane foam block manufactured in a rectangular sealed mold. Full article

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO₂ emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO₂ emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s² on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO₂ emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO₂ forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring. Full article

In the field of mechanical engineering, inclined rubber bearings reduce vertical stiffness through tilted arrangement to effectively isolate environmental vibrations. When applied to large-scale structural engineering, however, further attention must be paid to their vertical hysteretic performance under large deformation, so as to provide a basis for three-dimensional seismic isolation analysis of structures. Traditional seismic design often simplifies the vertical constitutive model of bearings as linear, while tests have shown that the vertical behavior of inclined rubber bearings exhibits significant asymmetric hysteretic characteristics, which cannot be accurately described by existing symmetric constitutive models. In this paper, vertical performance tests are further conducted on inclined rubber bearing specimens, and a modified hysteretic polynomial model is proposed to adapt it to the theoretical description of asymmetric vertical hysteretic behavior of inclined rubber bearings. Through parameter modification, device testing, and comparative analysis of results, the accuracy and effectiveness of the model are verified, providing a theoretical basis for its engineering application. Full article

This work constructs an orthogonal function system on bounded intervals $[0,R]$ associated with the Atangana–Baleanu–Caputo (ABC) fractional derivative for $\alpha \in (1/2,1)$. Starting from a fractional Laguerre-type equation involving the ABC operator, solutions are obtained via a generalized Frobenius method, yielding series representations with characteristic exponent $\alpha -1$. Rather than postulating a weight function by analogy with classical or Caputo settings, the weight is derived directly from the requirement that the underlying fractional operator be formally self-adjoint on a suitable admissible domain. This operator-theoretic approach leads to the explicit Mittag–Leffler weight $w_{\alpha }\left(x\right)=x^{-(2\alpha -1)}{E}_{\alpha }(-x^{\alpha })$, which intrinsically reflects the nonlocal memory structure of the ABC kernel. A similarity transformation removes the universal singular factor and produces regularized eigenfunctions that are continuous on $[0,R]$ and orthogonal in the weighted $L^2$ space. The weight identity and formal self-adjointness are rigorously verified through a right-Volterra uniqueness argument. Numerical experiments confirm orthogonality up to machine precision, demonstrate spectral convergence for a model ABC differential equation, and illustrate consistency with classical Laguerre polynomials in the limit $\alpha \to 1^{-}$. The resulting framework provides a self-consistent orthogonal system suitable for spectral approximations of problems governed by the ABC operator on bounded domains. Full article

Real-time lithology identification while drilling is widely applied in oil and gas exploration, development drilling, geo-steering, unconventional resource extraction, well logging, and environmental monitoring, enhancing efficiency and accuracy in subsurface operations. This study investigates the frequency characteristics of rock-drilling sounds generated during drilling operations and explores their potential for real-time lithology identification. Experiments were conducted using 8 mm and 14 mm drill bits at both high and low rotational speeds on four types of rock samples: sandstone, limestone, granite, and shaly sandstone. Sound signals were recorded both within the rock and in air using high-fidelity sensors. The results reveal distinct frequency patterns for each rock type, with sandstone exhibiting dominant low-frequency energy, limestone and granite showing broader frequency bands with strong high-frequency components, and shaly sandstone displaying a mix of low- and high-frequency energy. Quadratic polynomial regression models between the Vp or Vs and the peak frequencies of the four distinct rock samples are built, and the corresponding coefficients of determination are 0.9878 and 0.9799. The study also demonstrates that drilling parameters, such as drill bit diameter and revolutions per minute (RPM), significantly influence the frequency distribution of rock-drilling sounds, with larger drill bits and higher RPMs producing broader frequency bands and stronger high-frequency energy. Comparisons between in-rock and in-air recordings show that the latter captures richer high-frequency information, though the overall trends remain consistent. These findings provide an experimental foundation for using rock-breaking sounds as a potential tool for lithology identification during drilling operations. The study highlights the importance of considering rock heterogeneity and drilling conditions when interpreting acoustic data and suggests future work to validate the method in field conditions and integrate advanced data processing techniques. Full article

Background: Intermaxillary elastics are widely used in orthodontics to deliver controlled forces for malocclusion correction, aiding in the correction of anteroposterior, vertical, or transverse problems. Despite their clinical relevance, comprehensive mechanical characterization remains limited. Objective: This study aimed to evaluate the mechanical properties of nine types of intermaxillary elastics available on the market to guide evidence-based clinical selection. Methods: Elastics were tested under uniaxial tensile loading following ISO 37:2011 and ISO 21606:2007, with six replicates per type. Load–displacement and stress–strain responses were analyzed, measuring peak force, elongation at rupture, work-to-rupture, and specific rupture work. Non-linear behavior was modeled using cubic polynomial regression, and normalized stress–strain curves enabled intrinsic material comparisons. One-way ANOVA with post-hoc tests assessed differences among elastics. Results: All elastics displayed characteristic non-linear elastomeric responses. Functional grouping distinguished short-displacement/high-stiffness, intermediate-displacement/moderate-stiffness, and long-displacement/high-capacity bands. Work-to-rupture, specific rupture work, and normalized stress–strain metrics varied significantly, reflecting differences in energy absorption and force delivery (p < 0.05). Conclusions: Mechanical characterization, including energy-based descriptors and normalized stress–strain analysis, supports informed elastic selection, enhancing orthodontic treatment predictability and patient safety. Full article

Folding-wing Unmanned Aerial Vehicles (UAVs) have become a key platform in modern aerial applications, owing to their superior portability and rapid deployment capabilities. While the tandem-wing configuration offers a compact solution for strict folding constraints, the resulting high wing loading necessitates a maximized lift coefficient (C_L) to ensure efficient low-speed loitering. This study presents an aerodynamic optimization framework aiming to maximize the C_L of a folding tandem-wing UAV. A combined optimization strategy integrating Optimal Latin Hypercube Sampling (OLHS), orthogonal polynomial surrogate models, and the Multi-Island Genetic Algorithm (MIGA) is established. With aft wing parameters determined, global sensitivity analysis identifies the fore wing span as the dominant factor, contributing 47.40% to lift performance. Crucially, although vertical separation contributes only 6.53% to C_L and sweep angle just −1.22% to drag coefficient, their strong interaction effects with wing span confirm their non-negligible role. Finally, the flow field characteristics at the wing root of the optimized configuration undergo significant changes, resulting in a 4.28% increase in the C_L. This work validates the important role of parameter interaction effects in aerodynamic optimization and provides a theoretical basis for the design of geometrically constrained aerial vehicles requiring high lift coefficients. Full article

A major quality challenge in the application of titanium alloys is the persistence of substances known as “hard-alpha inclusions”. Although hard-alpha inclusions are extremely rare and typically small in size in high-quality titanium alloys for aero-engine disks, their hard and brittle nature poses a non-negligible threat to the structural integrity of the disks. Due to the extreme scarcity of natural hard-alpha inclusions, most previous studies have focused on “synthetic dense hard-alpha particles” rather than “real porous hard-alpha inclusions”, inevitably over-looking the differences between them. In this work, a method of introducing titanium nitride sponge preforms into the electrode preparation step of the smelting process is proposed and implemented, successfully fabricating real porous hard-alpha inclusions in TC4 titanium alloy disks. On this basis, the detection characteristics of ultrasonic non-destructive testing for such porous hard-alpha inclusions are investigated, and a probability of detection (POD) model for these defects is established for the first time. A defect distribution model of porous hard-alpha inclusions for the probabilistic damage tolerance assessment of disks is also derived. This work reveals that, unlike the “linear” behavior of traditional models, the new defect distribution model adheres to a “cubic polynomial” relationship. Full article