Search Results (914)

Overfall flow, characterized by high Froude numbers and intense turbulence, generates boundary shear stress on vertical surfaces, which is considered the direct cause of headcut erosion. This study aims to analyze the hydraulic characteristics of nappe flow over a vertical or near-vertical overfall. Detailed experiments using hot-film anemometry were conducted in an indoor flume to examine the shear stress distribution on vertical surfaces under varying flow rates, overfall heights, and backwater depths. The results show that when the jet dynamic pressure head is less than the backwater depth, the dimensionless relative shear stress and relative depth relationship can be fitted with a beta probability density function. When the dynamic pressure head exceeds the backwater depth, the distribution follows a cubic polynomial form. Dimensional analysis and flow trajectory calculation methods were used to establish shear stress distribution formulas, with determination coefficients of 0.829 and 0.652, and the mean absolute percentage error (MAPE) between the measured and predicted values being 0.106 and 0.081, respectively. The findings provide valuable insights into the effects of complex flow structures on shear stress and offer essential support for the development of scour models for overfall structures. Full article

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management. Full article

The shape and position information of towed streamers is crucial for both implementing marine seismic exploration operations and analyzing exploration data. Streamer positioning accuracy directly impacts the quality and reliability of seismic imaging. Existing polynomial curve models exhibit deviations between the calculated and actual shapes during streamer turning. This paper proposes a segmented fitting positioning model based on spline curves. It is mathematically rigorous and applicable to complex scenarios. First, the specific function expression of the spline curve model is constructed. Then, using a cubic spline as an example, the segmented fitting method is explained, incorporating smoothness constraints at the connection points. The error equations for positioning observations and the calculation processes for curve parameters and hydrophone coordinates are derived. Finally, the model is verified through simulations and field tests. The experimental results show that, compared with the polynomial curve model, the spline curve model improves positioning accuracy by 47.1% in simulations involving six streamers and by 20.0% and 35.0% in field tests with six and ten streamers, respectively. In straight scenarios, both models perform similarly. Thus, the spline model can effectively reduce the modeling errors of the polynomial curve model under high-curvature conditions. Full article

Hydrological time series frequently exhibit periodic trends with variables such as rainfall, runoff, and evaporation rates often following annual cycles. Seasonal variations further contribute to the complexity of these data sets. A critical aspect of analyzing such phenomena is estimating realistic return intervals, making the precise determination of these values essential. Given this importance, selecting an appropriate probability distribution is paramount. To address this need, we introduce a flexible probability model specifically designed to capture periodicity in hydrological data. We thoroughly examine its fundamental mathematical and statistical properties, including the asymptotic behavior of the probability density function (PDF) and hazard rate function (HRF), to enhance predictive accuracy. Our analysis reveals that the PDF exhibits polynomial decay as $x\to \infty$, ensuring heavy-tailed behavior suitable for extreme events. The HRF demonstrates decreasing or non-monotonic trends, reflecting variable failure risks over time. Additionally, we conduct a simulation study to evaluate the performance of the estimation method. Based on these results, we refine return period estimates, providing more reliable and robust hydrological assessments. This approach ensures that the model not only fits observed data but also captures the underlying dynamics of hydrological extremes. Full article

The cislunar space navigation satellite system is essential infrastructure for lunar exploration in the next phase. It relies on high-precision orbit determination to provide the reference of time and space. This paper focuses on constructing a navigation constellation using special orbital locations such as Earth–Moon libration points and distant retrograde orbits (DRO), and it discusses the simplification of planetary perturbation models for their autonomous orbit determination on board. The gravitational perturbations exerted by major solar system bodies on spacecraft are first analyzed. The minimum perturbation required to maintain a precision of 10 m during a 30-day orbit extrapolation is calculated, followed by a simulation analysis. The results indicate that considering only gravitational perturbations from the Moon, Sun, Venus, Saturn, and Jupiter is sufficient to maintain orbital prediction accuracy within 10 m over 30 days. Based on these findings, a method for simplifying the ephemeris is proposed, which employs Hermite interpolation for the positions of the Sun and Moon at fixed time intervals, replacing the traditional Chebyshev polynomial fitting used in the JPL DE ephemeris. Several simplified schemes with varying time intervals and orders are designed. The simulation results of the inter-satellite links show that, with a 6-day orbit arc length, a 1-day lunar interpolation interval, and a 5-day solar interpolation interval, the accuracy loss for cislunar space navigation satellites remains within the meter level, while memory usage is reduced by approximately 60%. Full article

Bragg gratings are fundamental components in a wide range of sensing applications due to their high sensitivity and tunability. In this work, we present an augmented Bayesian approach for efficiently acquiring limited but highly informative training data for machine learning models in the design and simulation of Bragg grating sensors. Our method integrates a distance-based diversity criterion with Bayesian optimization to identify and prioritize the most informative design points. Specifically, when multiple candidates exhibit similar acquisition values, the algorithm selects the point that is farthest from the existing dataset to enhance diversity and coverage. We apply this strategy to the Bragg grating design space, where various analytical functions are fitted to the optical response. To assess the influence of output complexity on model performance, we compare different fit functions, including polynomial models of varying orders and Gaussian functions. Results demonstrate that emphasizing output diversity during the initial stages of data acquisition significantly improves performance, especially for complex optical responses. This approach offers a scalable and efficient framework for generating high-quality simulation data in data-scarce scenarios, with direct implications for the design and optimization of next-generation Bragg grating-based sensors. Full article

Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R²) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Full article

Thermal management is an area of study in electronics focused on managing temperature to improve reliability and efficiency. When temperatures are too high, cooling systems are activated to prevent overheating, which can lead to reliability issues. To monitor the temperatures, sensors are often placed on-chip near hotspot locations. These sensors should be very small to allow them to be placed among compact, high-activity circuits. Often, they are connected to a central control circuit located far away from the hot spot locations where more area is available. This paper proposes sensing units for a novel temperature sensing architecture in the TSMC 180 nm process. This architecture functions by approximating the current through the sensing unit at a reference voltage, which is used to approximate the temperature in the digital back end using fitting functions. Sensing units are selected based on how well its temperature–current relationship can be modeled, sensing unit area, and power consumption. Many sensing units will be experimented with at different reference voltages. These temperature–current curves will be modeled with various fitting functions. The sensing unit selected is a diode-connected p-type MOSFET (Metal Oxide Semiconductor Field Effect Transistor) with a size of W = 400 nm, L = 180 nm. This sensing unit is exceptionally small compared to existing work because it does not rely on multiple devices at the sensing unit location to generate a PTAT or IPTAT signal like most work in this area. The temperature–current relationship of this device can also be modeled using a 2nd order polynomial, requiring a minimal number of trim temperatures. Its temperature error is small, and the power consumption is low. The range of currents for this sensing unit could be reasonably made on an IDAC. Full article

Fiber Bragg grating (FBG) exhibits strong resistance to electromagnetic interference and excellent linear strain response, making it highly promising for structural health monitoring (SHM) in pavement. This research investigates the strain transfer characteristics of embedded FBG in pavement structure and materials by using the relevant theoretical models. Results indicate adhesive layer thickness and sheath modulus are the primary factors influencing the strain transfer coefficient. A thinner adhesive layer and high modulus of sheath enhance the coefficient. Additionally, the strain distribution of sheath significantly affects the transfer efficiency. When the stress level near the grating region is lower than the both ends, the coefficient increases and even exceeds 1, which typically occurs under multi-axle conditions. As for asphalt mixture, high temperature leads to lower efficiency, while accumulated plastic strain improves it. Although the increased load frequency results a higher strain transfer coefficient, the magnitude of this change is negligible. By employing polynomial fitting to the sheath strain distribution, the boundary condition of theoretical equation could be removed. The theoretical and numerical results of strain transfer coefficient for pavement embedded FBG demonstrate good consistency, indicating the polynomial fitting is adoptable for the theoretical calculation with non-uniform strain distribution. This study utilizes the FEM to clarify the evolution of FBG strain transfer in pavement structures and materials, providing a theoretical basis for the design and implementation of embedded FBG in pavement. Full article

We propose the zero-inflated Polynomially Adjusted Poisson (zPAP) model. It extends the usual zero-inflated Poisson by multiplying the Poisson kernel with a nonnegative polynomial, enabling the model to handle extra zeros, overdispersion, skewness, and even multimodal counts. We derive the maximum-likelihood framework—including the log-likelihood and score equations under both general and regression settings—and fit zPAP to the zero-inflated, highly dispersed Fish Catch data as well as a synthetic bimodal mixture. In both cases, zPAP not only outperforms the standard zero-inflated Poisson model but also yields reliable inference via parametric bootstrap confidence intervals. Overall, zPAP is a clear and tractable tool for real-world count data with complex features. Full article

With the advancement of error correction techniques such as quadrature suppression and mode matching, the bias stability and overall accuracy of MEMS gyroscopes have been greatly improved. However, scale-factor nonlinearity often being underestimated has emerged as a critical barrier to further performance enhancement in high-precision MEMS gyroscopes. This study investigates the mechanism of scale-factor nonlinearity in closed-loop MEMS gyroscopes and introduces the concept of scale-factor repeatability error. A constraint relationship between scale-factor nonlinearity and repeatability is analytically established. Based on this insight, a composite compensation method incorporating initial calibration is proposed to enhance scale-factor linearity. By improving repeatability, the effectiveness and accuracy of polynomial fitting-based compensation are significantly improved. Experimental results show that the proposed method reduces the scale-factor nonlinearity error from 2232.039 ppm to 99.085 ppm, achieving a 22.5-fold improvement. The proposed method is also applicable to other MEMS gyroscopes with similar architectures and control strategies. Full article

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

Packaging equipment plays a crucial role in the semiconductor industry by enhancing product quality and reducing labor costs through automation. Research was conducted on IGBT module packaging equipment (an automatic pin insertion machine) during the pin assembly process of insulated gate bipolar transistor (IGBT) modules to improve productivity and product quality. First, the manual pin assembly process was divided into four stages: feeding, stabilizing, clamping, and inserting. Each stage was completed by separate cams, and corresponding step timing diagrams are drawn. The profiles of the four cams were designed and verified through theoretical calculations and kinematic simulations using a seventh-degree polynomial curve fitting method. Then, image algorithms were developed to detect pin tilt defects, pin tip defects, and to provide visual guidance for pin insertion. Finally, a pin insertion machine and its human–machine interaction interface were constructed. On-machine results show that the pin cutting pass rate reached 97%, the average insertion time for one pin was 2.84 s, the pass rate for pin insertion reached 99.75%, and the pin image guidance accuracy was 0.02 mm. Therefore, the designed pin assembly machine can reliably and consistently perform the pin insertion task, providing theoretical and experimental insights for the automated production of IGBT modules. Full article

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. Fourier Transform Infrared (FTIR) spectroscopy, due to its high sensitivity, non-destructive nature, and potential for online monitoring, has emerged as a key technique in gas detection. However, the complex underground environment often causes baseline drift in IR spectra. Furthermore, the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims to perform a quantitative analysis of coal mine gases by FTIR. It utilized the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of coal mine gases, they could be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines, including the absorption peak and its adjacent troughs, were selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method bassed on the impact values of variables and population analysis was applied to select variables from the spectral data. The selected variables were then used as input features for building a model with a backpropagation (BP) neural network. Finally, the proposed method was validated using standard gases. Experimental results show detection limits of 0.5 ppm for CH₄, 1 ppm for C₂H₆, 0.5 ppm for C₃H₈, 0.5 ppm for n-C₄H₁₀, 0.5 ppm for i-C₄H₁₀, 0.5 ppm for C₂H₄, 0.2 ppm for C₂H₂, 0.5 ppm for C₃H₆, 1 ppm for CO, 0.5 ppm for CO₂, and 0.1 ppm for SF₆, with quantification limits below 10 ppm for all gases. Experimental results show that the absolute error is less than 0.3% of the full scale (F.S.) and the relative error is within 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance. Full article

Strawberries have a short shelf-life leading to food loss and waste when production unexpectedly exceeds demand. PGRs may have potential to delay production and reduce food loss and waste, but no PGRs are available for delaying strawberry production. The aim of this preliminary study was to investigate re-purposing a stomatal blocking film antitranspirant polymer as a PGR to temporarily delay production. Poly-1-p-menthene or water was applied during early fruit ripening in two glasshouse experiments, one on a June-bearer cultivar and one on an everbearer cultivar. Ripe strawberries were harvested during the next 23 days, the cumulative yield was recorded, and the production curves were fitted using polynomial regression in groups. The statistical analysis showed that cubic polynomial regression curves could be fitted separately to each treatment. Application of the blocker delayed the production of both cultivars by 1–2 days during the period of rapid berry production. The delay diminished and cumulative yield returned to the water-treated value by 13 and 18 days after application in the June-bearer and everbearer cultivars, respectively. At 23 days after application, the blocker gave 8% greater cumulative yield in the June-bearer, but not in the everbearer. It was concluded that, if a greater delay could be achieved, there may be potential to use stomatal blockers as PGRs in some cultivars of strawberry to delay production and reduce food loss and waste when unanticipated lower demand occurs. Full article