Search Results (2,163)

Maternal health care during labor requires the continuous and reliable monitoring of analgesic procedures, yet conventional systems are often subjective, indirect, and operator-dependent. Infrared thermography (IRT) offers a promising non-invasive approach for labor epidural analgesia (LEA) monitoring, but its practical implementation is hindered by clinical and hardware limitations. This work presents a novel artificial intelligence-driven mobile platform to overcome these hurdles. The proposed solution integrates a lightweight deep learning model for semantic segmentation, a B-spline-based free-form deformation (FFD) approach for non-rigid dermatome registration, and efficient on-device inference. Our analysis identified a U-Net with a MobileNetV3 backbone as the optimal architecture, achieving a high Dice score of 0.97 and a 4.5% intersection over union (IoU) gain over heavier backbones while being 73% more parameter-efficient. The entire AI pipeline is deployed on a commercial smartphone via TensorFlow Lite, achieving an on-device inference time of approximately two seconds per image. Deployed within a user-friendly interface, our approach provides straightforward feedback to support decision making in labor management. By integrating thermal imaging with deep learning and mobile deployment, the proposed system provides a practical solution to enhance maternal care. By offering a quantitative, automated tool, this work demonstrates a viable pathway to augment or replace subjective clinical assessments with objective, data-driven monitoring, bridging the gap between advanced AI research and point-of-care practice in obstetric anesthesia. Full article

This paper presents a novel B-spline wavelet-based scheme for solving multi-term time–space variable-order fractional nonlinear diffusion-wave equations. By combining semi-orthogonal B-spline wavelets with a collocation approach and a quasilinearization technique, we transform the original problem into a system of algebraic equations. To enhance the computational efficiency, we derive the operational matrix formulation of the proposed scheme. We provide a rigorous convergence analysis of the method and demonstrate its accuracy and effectiveness through numerical experiments. The results confirm the robustness and computational advantages of our approach for solving this class of fractional differential equations. Full article

Background: Previous studies have shown that a diet with inflammatory and antioxidant properties can alter the risk of cognitive impairment. There are few studies using a large sample of the Chinese population. The specific relationship between inflammation, an antioxidant diet, and cognitive impairment remains unclear, and the potential impact of metabolic disorders remains to be determined. Methods: This is a cross-sectional study, with data from the China Health and Nutrition Survey (CHNS) in 2018. Individual and combined effects of the dietary inflammation index (DII) and composite dietary antioxidant index (CDAI) on cognitive impairment were assessed by binary logistic regression models. Nonlinear correlations and the inflection point were explored using restricted cubic splines (RCSs), and the mediation effects of triglyceride glucose–body mass index (TyG-BMI) were explored in greater depth using causal mediation analysis. Results: An increased CDAI was associated with a significantly decreased risk of cognitive impairment, at 0.68 (95%CI: 0.499–0.928). Contrary to this, the DII was positively associated with the risk of cognitive impairment, at 1.289 (95%CI: 1.03–1.613). The joint effects of the DII and CDAI indicated the minimal hazard effects on the risk of cognitive (0.787 (95%CI: 0.622–0.995)) impairment in subjects with low_DII + high_CDAI when compared with those with high_DII + low_CDAI. Furthermore, a significant nonlinear relationship was found between the CDAI and the risk of cognitive impairment, exhibiting an “L”-shaped curve (p-overall = 0.001, p-nonlinear = 0.007). However, no evidence was found for a nonlinear relationship between the DII and the risk of cognitive impairment. The mediation analysis did not reveal a mediating effect of TyG-BMI on the association between the CDAI and DII scores and the risk of cognitive impairment. Conclusions: Findings revealed that the CDAI could mitigate the adverse consequences of the DII on cognitive decline, which offers new insights into preventing early cognitive impairment through dietary intervention. Full article

This study was conducted to (i) determine the association between live body weight (BW) and biometric traits, (ii) examine the effect of biometric traits on BW of Tswana sheep using MARS and CART data mining algorithms, (iii) compare the performance of the algorithms and, finally, select the best algorithm for predicting BW in Tswana sheep. BW and sixteen biometric traits were measured from 392 Tswana sheep (males = 85 and females = 307) aged three to four years. Pearson’s correlation coefficients were used to establish the relationship between BW and biometric traits. The goodness of fit criteria were computed to assess the predictive performance of the data mining algorithms and select the best-fit model for predicting BW. The results showed that BW had a positive and significant correlation with heart girth (HG) (r = 0.99); thus, HG was used as a sole predictor of BW. The goodness of fit results indicated that MARS has a higher predictive performance than the CART algorithm, suggesting that the MARS algorithm can be used to predict BW Tswana sheep. These findings are an important statistical tool for the selection and concurrent improvement of useful biometric traits in genetic improvement programs to improve BW in Tswana sheep. Full article

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by spline interpolation in joint space (cubic or quintic interpolation), so that the distances traveled are shorter. The principle of least action is used as a theoretical foundation trying to find the best cost function in terms of trajectory lengths using. In the pursuit of minimizing this cost function, an iterative method is applied. Initial trajectories are split into multiple internal nodes that are displaced little by little from their initial positions, recomposing trajectories that pass through these displaced nodes at every iteration. The purpose of this paper is to demonstrate that by post-processing trajectories initially generated by the usual spline interpolation in joint space, alternative, shorter variants can be obtained. Full article

Background: Age is a well-established determinant of sepsis outcomes, often integrated into severity scoring systems. However, most studies focus on critically ill patients in intensive care units (ICUs), with limited insight into how age influences mortality in non-ICU settings, particularly across the full adult lifespan. Objective: To investigate the relationship between age and in-hospital mortality in patients with sepsis hospitalized in internal medicine wards, using age-stratified logistic and spline regression models. Methods: We conducted a retrospective, multicenter cohort study involving 4300 adult patients admitted to internal medicine wards at eight academic hospitals affiliated with Clalit Health Services in Israel between December 2001 and October 2020. All patients were diagnosed with sepsis during hospitalization and died during their hospital stay. Patients were stratified into seven age groups (18–34, 35–44, 45–54, 55–64, 65–74, 75–84, >85 years). Logistic regression identified age-specific comorbidities associated with mortality. Adjusted spline regression models were used to estimate mortality probabilities across age ranges. Results: The cohort had a mean age at death of 78.84 years, and 51.7% were female. Mortality probability increased with age but demonstrated non-linear trends. Sharp fluctuations in predicted mortality were observed in middle-aged groups (especially ages 45–54), with peaks not captured in conventional binary or linear models. Hematologic and solid neoplasms were strongly associated with mortality in younger groups, while cardiovascular comorbidities such as heart failure and atrial fibrillation were more prominent in older adults. Conclusions: Age is a major determinant of in-hospital mortality in septic patients on internal medicine wards, but its effect is non-linear and age-specific. Our findings highlight a unique population of patients with severe sepsis not managed in critical care settings and underscore the need for more nuanced, age-stratified risk assessment models outside of the ICU. Full article

Background/Objectives: Inflammation plays a key role in the pathophysiology of acute myocardial infarction (AMI). Yet static measures of C-reactive protein (CRP) provide limited prognostic information. CRP velocity (CRPv), which reflects the rate of CRP rise within the first 24 h, may better depict the dynamic inflammatory response. To investigate the prognostic role of CRPv in patients presenting with a first AMI. Methods: Consecutive patients presenting with first AMI were enrolled. CRPv was calculated as the difference between CRP at admission and after 24 ± 8 h, divided by time. A prognostic CRPv cut-off was derived from spline curve analysis to dichotomize the population. Patients were followed up for the primary composite endpoint of cardiovascular death, non-fatal AMI, and hospitalization for heart failure. Results: Among 604 patients, 189 (31.3%) had CRPv ≥ 1.36 mg/L/h and 415 (68.7%) had CRPv < 1.36 mg/L/h. Higher hs-cTnT (adjusted odds ratio [aOR] 2.552, 95% CI, 1.520–4.286; p < 0.001) and NT-proBNP (aOR 2.229, 95% CI, 1.241–4.002; p = 0.007) were independently associated with CRPv ≥ 1.36 mg/L/h. At a median follow-up of 13.8 months, 115 patients (19.0%) reached the primary composite endpoint. High CRPv patients had significantly lower event-free survival rate than low CRPv patients (66.7% vs. 85.5%, log-rank p < 0.001). CRPv independently predicted the primary composite endpoint [adjusted hazard ratio 1.226, 95% CI 1.102–1.364, p < 0.001]. Adding CRPv on top of clinical, echocardiographic, and biochemical risk factors significantly improved model discrimination (p < 0.001), whereas single CRP on admission (p = 0.947) or CRP 24 ± 8 h from admission (p = 0.064) did not. Conclusions: CRPv appears to be a robust predictor of adverse outcomes in first AMI patients, offering incremental prognostic value beyond established clinical and biomarker indices. Its feasibility and low cost support its integration into early clinical risk stratification. Full article

This paper tackles a core challenge in reverse engineering: high-fidelity reconstruction of continuous B-spline surfaces from discrete point clouds, where optimal knot placement remains pivotal yet not fully resolved. We propose a new fitting method based on the Hungry Predation Algorithm (HPA) to improve efficiency, accuracy, and robustness. This method introduces a hybrid knot-guidance strategy that combines geometry-aware preselection with a complexity-driven probabilistic distribution to address knot placement. On the optimization side, HPA simulates starvation-driven predator–prey dynamics to enhance global search capability, maintain population diversity, and accelerate convergence. We also develop an adaptive parameter adjustment framework that automatically tunes key settings according to surface complexity and accuracy thresholds. Comparative experiments against classical approaches, six state-of-the-art optimizers, and the commercial CAD system CATIA demonstrate HPA’s superiority in control-point reduction, fitting accuracy, and computational efficiency. This method shows high applicability to engineering-scale tasks (e.g., ship hull design), where the point-to-surface RMSE (e.g., <10⁻³ L_max) achieved satisfies stringent requirements for downstream hydrodynamic performance analysis and manufacturing. Full article

Free-form grid structures offer both aesthetic appeal and structural efficiency in long-span roof design and application, yet the potential of morphological design to optimize thermal performance has been long overlooked. This study proposes a multi-objective synergistic optimization framework which can improve the thermal environment and mechanical performance simultaneously for the roof. Focusing on public buildings in hot–humid climates, the research investigates the impact of roof geometry on indoor temperature under extreme thermal loading conditions and long-term thermal loading conditions. Furthermore, the evolution of thermal performance during mechanical performance-driven surface optimization is systematically analyzed. Subsequently, a dynamic proportional adjustment factor is introduced to explore the performance of the optimized results under different performance weights, with thermal and mechanical performance serving as the optimization objectives. Results demonstrate that thermal performance-driven optimization generates saddle-shaped free-form surfaces with alternating peak–valley configurations to achieve self-shadowing effects, reducing indoor temperature by approximately 2 °C but significantly compromising structural stiffness. Conversely, strain energy minimization yields moderate indoor temperature reductions, revealing a positive correlation between strain energy decrease and thermal performance improvement. In the multi-objective optimization considering thermal and mechanical properties, when the strain energy ratio is 0.5–0.7 (optimization balance zone), the indoor temperature decreases, while the structural stiffness and stability bearing capacity increase. This study provides a morphological–structural–environmental synergistic design reference for low-carbon long-span building roofs. Full article

Cow bunching is a behavioral phenomenon where cattle aggregate in tight groups to protect themselves from biting by stable flies (Stomoxys calcitrans L.). The incidence of bunching varies between dairies and even among pens within the same dairy, as it is associated with the location-specific biting intensity of stable flies, which largely varies with dairy management and local environmental factors. Bunching may be associated with decreased feeding and laying times, as well as heat stress due to cattle aggregation. Thus, bunching may affect dairy cows’ milk production by reducing dry matter intake and rumination. To our knowledge, there are no previous studies specifically addressing the effect of cow bunching on milk production in lactating dairy cows. The objectives of our study were to estimate the economic impact of cow bunching against stable flies on milk production on a commercial California dairy and to estimate the economic losses associated with cow bunching and stable fly biting per cow per year. A longitudinal study was conducted from 1 May 2017 through 31 July 2017 on a 5000-cow Holstein herd housed in free stall pens in Tulare County, California. Pen-level cow bunching in four lactating cow pens was recorded weekly for 12 weeks. Bunching observations each day were matched to daily milk records for the study dairy. Two-piece spline linear mixed models were used to estimate the impact of cow bunching and stable fly counts on milk production. Cows in pens where bunching occurred experienced a significant milk reduction of 0.45 kg ± 0.104 (SE) per cow (p < 0.01) on the day of bunching in comparison to cows in pens without bunching. There was a significant reduction of 0.6 kg/cow/day in milk production associated with each increase in one stable fly per cow leg (standard metric for recording stable fly biting activity) after adjusting for parity, temperature humidity index (THI), and days in milk (DIM). Based on the economic analysis conducted on weekly bunching and fly counts, modeled milk production losses were reported as weekly loss in milk revenue per cow. The estimated economic loss associated with cow bunching and stable fly counts was highest during the last week of May (USD 0.34/cow/week and USD 1.86/cow/week, respectively) and was lowest during the last week of July (USD 0.03/cow/week and USD 0.29/cow/week, respectively). To mitigate the most substantial economic loss, dairy producers should focus their efforts on controlling stable flies during the early stable fly season, when stable fly abundance tends to be highest. Full article

Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests are widely distributed in this region, and phenology extraction based on vegetation indices has certain limitations, while SIF-based phenology extraction offers a viable alternative. This study first evaluated phenological results derived from three solar-induced chlorophyll fluorescence (SIF) datasets, six curve-fitting methods, and five phenological extraction thresholds at flux sites to determine the optimal threshold and SIF data for phenological indicator extraction. Secondly, uncertainties in phenological indicators obtained from the six fitting methods were quantified at the regional scale. Finally, based on the optimal phenological results, the spatiotemporal variations in phenology in Southwest China were systematically analyzed. Results show: (1) Optimal thresholds are 20% for the start of growing season (SOS) and 30% for the end of growing season (EOS), with GOSIF best for SOS and EOS, and CSIF for the peak of growing season (POS). (2) Cubic Smoothing Spline (CS) has the lowest uncertainty for SOS, while Savitzky–Golay Filter (SG) has the lowest for EOS and POS. (3) Phenology exhibits significant spatial heterogeneity, with SOS and POS generally showing an advancing trend, and EOS and length of growing season (LOS) showing a delaying (extending) trend. This study provides a reference for phenology extraction in regions with frequent cloud cover and widespread evergreen vegetation, supporting effective assessment of regional ecosystem dynamics and carbon balance. Full article

Background/Objectives: In computer graphics, virtual worlds are constructed and visualized through algorithmic processes. These environments are typically populated with objects defined by mathematical models, traditionally based on Euclidean geometry. However, there is increasing interest in exploring non-Euclidean geometries, which require adaptations of the modeling techniques used in Euclidean spaces. Methods: This paper focuses on defining parametric curves and surfaces within elliptic and hyperbolic geometries. We explore free-form splines interpreted as hierarchical motions along geodesics. Translation, rotation, and ruling are managed through supplementary curves to generate surfaces. We also discuss how to compute normal vectors, which are essential for animation and lighting. The rendering approach we adopt aligns with physical principles, assuming that light follows geodesic paths. Results: We extend the Kochanek–Bartels spline to both elliptic and hyperbolic geometries using a sequence of geodesic-based interpolations. Simple recursive formulas are introduced for derivative calculations. With well-defined translation and rotation in these curved spaces, we demonstrate the creation of ruled, extruded, and rotational surfaces. These results are showcased through a virtual reality application designed to navigate and visualize non-Euclidean spaces. Full article

Objective: The purpose of this study is to present a kyphosis measurement method based on quadratic spline fitting through three key vertebral landmarks: T12, T8 and T4. This approach aims to capture thoracic spine curvature more continuously and accurately than traditional methods such as the Cobb angle and circle fitting. Methods: A dataset of 560 lateral thoracic spine radiographs was retrospectively analyzed, including cases of postural kyphosis, Scheuermann’s disease, osteoporosis-induced kyphosis and ankylosing spondylitis. Two trained raters independently performed three repeated landmark annotations per image. The kyphosis angle was computed using two methods: (1) a quadratic spline fitted through the three landmarks, with the angle derived from tangent vectors at T12 and T4; and (2) a least-squares circle fit with the angle subtended between T12 and T4. Agreement with reference Cobb angles was evaluated using Pearson correlation, MAE, RMSE, ROC analysis and Bland–Altman plots. Reliability was assessed using intraclass correlation coefficients (ICC). Results: Both methods showed excellent intra- and inter-rater reliability (ICC ≥ 0.967). The spline method achieved lower MAE (5.81°), lower RMSE (8.94°) and smaller bias compared to the circle method. Both methods showed strong correlation with Cobb angles (r ≥ 0.851) and excellent classification performance (AUC > 0.950). Conclusions: Spline-based kyphosis measurement is accurate, reliable and particularly robust in cases with severe spinal deformity. Significance: This method supports automated, reproducible kyphosis assessment and may enhance clinical evaluation of spinal curvature using artificial intelligence-driven image analysis. Full article

The study focuses on the experimental investigation of the impact of using coconut oil (CO) as a phase-change material (PCM) for heat storage on the root-zone temperature within a greenhouse in Adana, Türkiye. The study examines the efficacy of PCM as latent heat-storage material and predicts root-zone temperature using three machine learning algorithms. The dataset used in the analysis consists of 2658 data at hourly resolution with six variables from February to April in 2022. A greenhouse with PCM shows a remarkable increase in both ambient (0.9–4.1 °C) and root-zone temperatures (1.1–1.6 °C) especially during the periods without sunlight compared to a conventional greenhouse. Machine learning algorithms used in this study include Multivariate Adaptive Regression Splines (MARS), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Hyperparameter tuning was performed for all three models to control model complexity, flexibility, learning rate, and regularization level, thereby preventing overfitting and underfitting. Among these algorithms, R² values for testing data listed from largest to smallest are MARS (0.95), SVR (0.96), and XGBoost (0.97), respectively. The results emphasize the potential of machine learning approaches for applying thermal energy storage systems to agricultural greenhouses. In addition, it provides insight into a net-zero energy greenhouse approach by storing heat in a bio-based PCM, alongside its implementation and operational procedures. Full article

Understanding the impact of hydric stress on medicinal plants in the context of climate change is becoming increasingly important. This study aimed to assess the quality of a seed lot of Agastache mexicana subsp. mexicana (Amm) through a novel calculation of the Vigour Index on time basis (${VI}_T$). The evaluation was based on relationships among plant height, leaf number, survival time, and plant density across six irrigation regimes, referred to as stages, which differed in the timing and quantity of water, designed to impose water stress from seedling emergence until plant death. To maximise growth and survival time, we utilised two input factors: Artificial Shade Levels (ASLs) of 38%, 87%, and 94%, as well as Silicon Dioxide Levels (SDLs) of 0.0%, 0.2%, 0.4%, and 0.8%. The effects of these treatments were measured using the Survival Index (SI) and the ${VI}_T$. The plants achieved their highest SI and ${VI}_T$ values influenced by minimum mortality and maximum height and leaf number in stage three. This behaviour aligned with the field capacity of the substrate, supporting the evaluation of stages one and two as waterlogging stress, while the remaining stages were classified as drought stress. The ${VI}_T$ results showed statistically significant effects from ASL, particularly at 94%. However, the ${VI}_T$ in relation to SDL was not statistically significant. The ${VI}_T$ measurements were visualised using spline interpolation, a method that provides an effective approach to quantify adverse conditions affecting Amm’s development and that it can support to identify the hydric stresses type. Full article