Search Results (260)

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

In the realm of 3D curve reconstruction, Non-Uniform Rational B-Splines (NURBSs) offer a versatile mathematical tool due to their ability to precisely represent complex geometries. However, achieving high fitting accuracy in stereo-based applications remains challenging, primarily due to the nonlinear nature of weight optimization. This study introduces an enhanced iterative strategy that leverages the geometric significance of NURBS weights to incrementally refine curve fitting. By formulating an inverse optimization problem guided by model deformation principles, the proposed method progressively adjusts weights to minimize reprojection error. Experimental evaluations confirm the method’s convergence and demonstrate its superiority in fitting accuracy when compared to conventional optimization techniques. Full article

Sugarcane is mostly planted in rows, and the accurate identification of crop rows is important for the autonomous navigation of agricultural machines. Especially in the elongation period of sugarcane, accurate row identification helps in weed control and the removal of ineffective tillers in the field. However, sugarcane leaves and stalks intertwine and overlap at this stage. They can form a complex occlusion structure, which poses a greater challenge to target detection. To address this challenge, this paper proposes an improved target detection method, SugarRow-YOLO, based on the YOLOv11n model. The method aims to achieve accurate sugarcane identification and provide basic support for subsequent sugarcane row detection. This model introduces the WTConv convolutional modules to expand the sensory field and improve computational efficiency, adopts the iRMB inverted residual block attention mechanism to enhance the modeling capability of crop spatial structure, and uses the UIOU loss function to effectively mitigate the misdetection and omission problem in the region of dense and overlapping targets. The experimental results show that SugarRow-YOLO performs well in the sugarcane target detection task, with a precision of 83%, recall of 87.8%, and mAP₅₀ and mAP_50-95 of 90.2% and 69.2%. In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. This method achieved 96.6% in the task, with high precision in sugarcane target detection and demonstrates excellent accuracy in sugarcane row fitting, offering robust technical support for the automation and intelligent advancement of agricultural operations. Full article

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

Integral field technology, as an advanced spectroscopic imaging technique, can be used to acquire the spatial and spectral information of the target area simultaneously. In this paper, we propose a method for the field reconstruction of characteristic wavelength bands of a space integral field spectrograph. The precise positioning of the image slicer is crucial to ensure that the spectrograph can accurately capture the position of each slicer in space. Firstly, the line spread function information and the characteristic location coordinates are obtained. Next, the positioning points of each group of image slicers under a specific spectral band are determined by quintic spline interpolation and a double-closed-loop optimization framework, thus establishing connection points for the responses of different image slicers. Then, the accuracy and reliability of the data are further improved by fitting the signal intensity of pixel points. Finally, the data of all image slicers are aligned to complete the field reconstruction of the characteristic wavelength bands of the space integral field spectrograph. This provides new ideas for the two-dimensional spatial reconstruction of spectrographs using image slicers as integral field units in specific spectral bands and accurately restores the two-dimensional spatial field observations of spatial integral field spectrographs. Full article

Given a triangular mesh in ${\mathbb{R}}^3$ with a family of points associated with its vertices along with vectors associated with its edges, we propose a novel technique for the construction of locally generated fitting parametrized G1-spline interpolation surfaces. The method consists of a G1 correction over the mesh edges of the mesh triangles, produced using reduced side derivatives (RSDs) introduced earlier by the author in terms of the barycentric weight functions. In the case of polynomial RSD shape functions, we establish polynomial edge corrections via an algorithm with an independent interest in determining the optimal GCD cofactors with the lowest degree for arbitrary families of polynomials. Full article

Over the past two decades, substantial efforts have been made to develop survival models for gap times between recurrent events. An emerging approach involves considering rate models derived from a non-homogeneous Poisson process, thus allowing the conditional distribution of a gap time given the previous recurrence time to be deduced. Under this approach, some parametric rate models have been proposed, differing in their distributional assumptions on gap times. In particular, the extended exponential–Poisson, Weibull and extended Chen–Poisson distributions have been considered. Alternatively, a flexible rate model using restricted cubic splines is proposed here to capture complex non-monotonic rate shapes. Moreover, a comprehensive comparison of parametric rate models is presented. The maximum likelihood method is applied for parameter estimation in the presence of right-censoring. It is shown that some models include important special cases that allow testing of the independence assumption between a gap time and the previous recurrence time. The likelihood ratio test, as well as two information criteria, are discussed for model selection. Model fit is assessed using Cox–Snell residuals. Applications to two well-known clinical data sets illustrate the comparative performance of both the existing and proposed models, as well as their practical relevance. Full article

Background and Objectives: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality. The cardiometabolic index (CMI) has been shown to be associated with metabolic disorders and mortality in general populations, but its role in ASCVD-specific mortality risk remains unexplored. Materials and Methods: This cohort study was based on the National Health and Nutrition Examination Survey (NHANES). Weighted Cox proportional hazards models were fitted to estimate the associations between CMI and mortality. Restricted cubic splines were used to explore nonlinear relationships. Subgroup analyses were used to investigate potential differences among specific ASCVD patients. Results: A total of 2157 patients with ASCVD were included. Over a median 83-month follow-up, 887 all-cause and 300 cardiovascular deaths occurred. Each unit increase in CMI was associated with an 11.3% increased risk of all-cause mortality (HR = 1.113, 95% CI: 1.112–1.115) and a 6.4% increased risk of cardiovascular mortality (HR = 1.064, 95% CI: 1.062–1.065). There was a nonlinear J-shaped relationship between CMI and all-cause mortality, while the risk of cardiovascular mortality increased linearly with increasing CMI. Conclusions: These findings underscore the importance of monitoring and managing CMI in patients with ASCVD in clinical practice and suggest that optimizing CMI levels may help reduce the risk of death and improve the long-term prognosis of patients. Full article

Post-traumatic stress disorder (PTSD) is a complex psychological disorder caused by multiple factors, which are not only related to individual psychological states but also closely linked to physiological responses, social environments, and personal experiences. Therefore, traditional single data source assessment methods are difficult to fully understand and evaluate the complexity of PTSD. To overcome this challenge, the focus of this study is on developing a PTSD risk assessment model based on multi-modal data fusion. The importance of multi-modal data fusion lies in its ability to integrate data from different dimensions and provide a more comprehensive PTSD risk assessment. For multi-modal data fusion, two sets of solutions are proposed: the first is to extract EEG features using B-spline basis functions, combined with questionnaire data, to construct a multi-modal Zero-Inflated Poisson regression model; the second is to build a multi-modal deep neural network fusion prediction model to automatically extract and fuse multi-modal data features. The results show that the multi-modal data model is more accurate than the single data model, with significantly improved prediction ability. Zero-inflated Poisson models are prone to over-fitting when data is limited, while deep neural network models show superior performance in both training and prediction sets, especially the Hybrid LSTM-FCNN model, which not only has high accuracy but also strong generalization ability. This study proves the potential of multi-modal data fusion in PTSD prediction, and the Hybrid LSTM-FCNN model stands out for its high accuracy and good generalization ability, providing scientific evidence for early warning of PTSD in rescue personnel. Future research can further explore model optimization and clinical applications to promote the mental health maintenance of rescue personnel. Full article

To address the challenge of optimizing system adaptability, disturbance rejection, control precision, and convergence speed simultaneously in four-wheel steering (4WS) stability control, a 4WS controller with a variable steering ratio (VSR) strategy and fast adaptive super-twisting (FAST) sliding mode control is proposed to control and output the steering angles of four wheels. The ideal VSR strategy is designed based on the constant yaw rate gain, and a cubic quasi-uniform B-spline curve fitting method is innovatively used to optimize the VSR curve, effectively mitigating steering fluctuations and obtaining precise reference front wheel angles. A controller based on FAST is designed for active rear wheel steering control using a symmetric 4WS vehicle model. Under double-lane change conditions with varying speeds, the simulations show that, compared with the constant steering ratio, the proposed VSR strategy enhances low-speed sensitivity and high-speed stability, improving the system’s adaptability to different operating conditions. Compared with conventional sliding mode control methods, the proposed FAST algorithm reduces chattering while increasing convergence speed and control precision. The VSR-FAST controller achieves optimization levels of more than 7.3% in sideslip angle and over 41% in yaw rate across different speeds, achieving an overall improvement in the stability control performance of the 4WS system. Full article

The vertical inline pump, a single-stage centrifugal pump with a bent elbow inlet, is widely used in marine engineering. The unique water inlet passage combined with uneven inflow at the impeller inlet tends to form an inlet vortex and secondary flow area, which reduces performance and causes vibration. To predict the performance of the elbow inline pump, this study uses spline curve fitting for the centerline and cross-sectional shape of the elbow passage. With four elbow inlet variables from experimental design as the input layer and targeting efficiency under pump operating conditions, a pump performance prediction model based on an improved sand cat swarm optimization algorithm combined with a BP neural network (MSCSO-BP) is proposed. Six test functions are used to effectively test the improved sand cat swarm optimization algorithm. The results show that compared to the unimproved algorithm, the improved algorithm has significantly faster convergence speed, shorter parameter optimization time, and higher accuracy. For more demanding multidimensional test functions, the improved optimization algorithm can more accurately find the optimal solution, enhancing the prediction accuracy and generalization ability of inline pump performance. This provides a more effective engineering solution for the design and optimization of inline pumps. Full article

In pipeline all-position welding processes, laser scanning provides critical geometric data of width-changing bevel morphology for welding torch swing control, yet conventional second-order derivative zero methods often yield pseudo-inflection points in practical applications. To address this, a third-order derivative weighted average threshold algorithm was developed, integrating image denoising, enhancement, and segmentation pre-processing with cubic spline fitting for precise bevel contour reconstruction. Bevel pixel points were captured by the laser sensor as inputs through the extracted second-order derivative eigenvalues to derive third-order derivative features, applying weighted threshold discrimination to accurately identify inflection points. Dual-angle sensors were implemented to synchronize laser-detected bevel geometry with real-time torch swing adjustments. Experimental results demonstrate that the system achieves a steady-state error of only 1.645% at the maximum swing width, a dynamic response time below 50 ms, and torch center trajectory tracking errors strictly constrained within ±0.1 mm. Compared to conventional methods, the proposed algorithm improves dynamic performance by 20.6% and exhibits unique adaptability to narrow-gap V-grooves. The results of these studies confirmed the ability of the method to provide real-time, accurate control for variable-width weld tracking, forming a swing-width adaptive control system. Full article

Background: Recent evidence has shown that insulin resistance (IR), a hallmark of nonalcoholic fatty liver disease, predicts bladder cancer (BC) presence. However, the best surrogate marker of IR in predicting BC is still unclear. This study examined the relationships among ten surrogate markers of IR and the presence of BC. Methods: Data from 209 patients admitted to two urology departments from September 2021 to October 2024 were retrospectively analyzed. Individuals (median age 70 years) were divided into two groups (123 and 86 patients, respectively) based on the presence/absence after cystoscopy/TURB of non-metastatic BC. Univariate logistic regression was used to determine the relationships between groups, and the following IR parameters: Triglyceride–Glucose (TyG) index, TyG-BMI, HOMA-IR HOMAB, MetS-IR, Single Point Insulin Sensitivity Estimator, Disposition Index, non-HDL/HDL, TG/HDL-C ratio and Lipoprotein Combine Index. Stepwise logistic regressions were carried out to evaluate the significant predictions and LASSO regression to confirm any significant variable(s). The predictive value of the index test for coexistent BC was evaluated using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). Results: The univariate analysis revealed that the TyG index and MetS-IR were associated with the BC presence. Specifically, the associations of the TyG index and MetS-IR were more significant in participants =/> 65 years old. In multivariate analysis, the stepwise logistic regression, evaluating the most representative variables at univariate analysis, revealed a prediction of BC by only TyG index (OR 2.51, p = 0.012), confirmed by LASSO regression, with an OR of 3.13, p = 0.004). Assessing the diagnostic reliability of TyG, it showed an interesting predictive value for the existence of BC (AUC = 0.60; 95% CI, 0.51–0.68, cut-off 8.50). Additionally, a restricted cubic spline model to fit the dose–response relationship between the values of the index text (TyG) and the BC evidenced the presence of a non-linear association, with a high predictive value of the first knot, corresponding to its 10th percentile. The decision curve analysis confirmed that the model (TyG) has utility in supporting clinical decisions. Conclusions: Compared to other surrogate markers of IR, the TyG index is effective in identifying individuals at risk for BC. A TyG threshold of 8.5 was highly sensitive for detecting BC subjects and may be suitable as an auxiliary diagnostic criterion for BC in adults, mainly if less than 65 years old. Full article

A harmonic drive (HD) is a precision reduction device widely utilized in the core joints of high-end equipment such as spacecraft and robots. The design of an excellent tooth profile is the core challenge related to the performance of HD. This investigation aims to propose a design method of a fully conjugated cycloid tooth profile (CTP) for HD. Firstly, the rationality of CTP use for HD is analyzed, and the cycloidal characteristics of the tooth trajectory are studied by use of canonical warping distance. Then, initial CTP equations are constructed, adopting the trajectory mapping results. Presetting the addendum CTP of circular spline, the conjugate CTP of flexspline is then designed using the envelope method. Subsequently, the envelope of the designed flexspline addendum is used to reverse-design the circular spline dedendum. The backlash is calculated to evaluate the CTPs designed with different radial displacement coefficients. Research shows that the tooth trajectory has cycloidal characteristics; therefore, the HDs that use CTP can realize a fully conjugate engagement. Moreover, the variable control parameters enable the proposed CTP expression to have excellent fitting characteristics, resulting in small and uniform mesh backlash distribution. The CTP is expected to become one of the ideal tooth profiles of HD. Full article

Maritime transportation is responsible for most global trade and is generally considered more environmentally efficient compared to other modes of transport, particularly for long-distance trade. With increasingly stringent emission regulations, however, accurately quantifying emissions and identifying their key determinants has become essential for effective environmental management. This study introduced a structured and comparative statistical modeling framework for ship-based emission modeling using gross tonnage (GT) as the primary predictor variable, due to its strong correlation with emission levels. Emissions for hydrocarbon (HC), carbon monoxide (CO), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and volatile organic compounds (VOC) were estimated using a bottom-up approach based on emission factors and formulas defined by the U.S. Environmental Protection Agency (EPA), using data from 38,304 vessel movements through the Bosphorus in 2021. These EPA-estimated values served as dependent variables in the modeling process. The modeling framework followed a three-step strategy: (1) outlier detection using Rosner’s test to reduce the influence of outliers on model accuracy, (2) curve fitting with 12 regression models representing four curve types—polynomial (e.g., linear, quadratic), concave/convex (e.g., exponential, logarithmic), sigmoidal (e.g., logistic, Gompertz, Weibull), and spline-based (e.g., cubic spline, natural spline)—to capture diverse functional relationships between GT and emissions, and (3) model comparison using difference performance metrics to ensure a comprehensive assessment of predictive accuracy, consistency, and bias. The findings revealed that nonlinear models outperformed polynomial models, with spline-based models—particularly natural spline and cubic spline—providing superior accuracy for HC, PM₁₀, SO₂, and VOC, and the Weibull model showing strong predictive performance for CO and NO_x. These results underscore the necessity of using pollutant-specific and flexible modeling strategies to capture the intricacies of maritime emission dynamics. By demonstrating the advantages of flexible functional forms over standard regression techniques, this study highlights the need for tailored modeling strategies to better capture the complex relationships in maritime emission data and offers a scalable and transferable framework that can be extended to other vessel types, emission datasets, or maritime regions. Full article