Search Results (58,502)

Background: Ginger contains gingerols, shagaols, paradols, gingerdiones, and terpenes, which have been shown to display anti-inflammatory properties and inhibit pain receptors. For this reason, ginger has been marketed as a natural analgesic. This study examined whether a specialized ginger extract obtained through supercritical CO₂ extraction and subsequent fermentation affects pain perception, functional capacity, and markers of inflammation. Methods: Thirty men and women (56.0 ± 9.0 years, 164.4 ± 14 cm, 86.5 ± 20.9 kg, 31.0 ± 7.5 kg/m²) with a history of mild to severe joint and muscle pain as well as inflammation participated in a placebo-controlled, randomized, parallel-arm study. Participants donated fasting blood, completed questionnaires, rated pain in the thighs to standardized pressure, and then completed squats/deep knee bends, while holding 30% of body mass, for 3 sets of 10 repetitions on days 0, 30, and 56 of supplementation. Participants repeated tests after 2 days of recovery following each testing session. Participants were matched by demographics and randomized to ingest 125 mg/d of a placebo or ginger (standardized to contain 10% total gingerols and no more than 3% total shogaols) for 58 days. Data were analyzed by a general linear model (GLM) analysis of variance with repeated measures, mean changes from the baseline with 95% confidence intervals, and chi-squared analysis. Results: There was evidence that ginger supplementation attenuated perceptions of muscle pain in the vastus medialis; improved ratings of pain, stiffness, and functional capacity; and affected several inflammatory markers (e.g., IL-6, INF-ϒ, TNF-α, and C-Reactive Protein concentrations), particularly following two days of recovery from resistance exercise. There was also evidence that ginger supplementation increased eosinophils and was associated with less frequent but not significantly different use of over-the-counter analgesics. Conclusions: Ginger supplementation (125 mg/d, providing 12.5 mg/d of gingerols) appears to have some favorable effects on perceptions of pain, functional capacity, and inflammatory markers in men and women experiencing mild to moderate muscle and joint pain. Registered clinical trial #ISRCTN74292348. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

Life Cycle Impact Assessments (LCIAs) examine the environmental impacts of products using life cycle inventories (LCIs) of quantified inputs and outputs of a product through its life cycle. Currently, estimated impacts from mining are dominated by long-term metal release from tailings due to inaccurate assumptions regarding metal release and transport within and from mine materials. A conceptual model approach is proposed to support the development of a new database of LCI data, applying mechanistic processes required for the release and transport of metals through tailings and categorizing model inputs into ‘bins’. The binning approach argues for accuracy over precision, noting that precise metal release rates are likely impossible with the often-limited data available. Three case studies show the range of forecasted metal release rates, where even after decades of monitoring within the tailings and underlying aquifer, metal release rates span several orders of magnitude (<100 mg/L to >100,000 mg/L sulfate at the Faro Mine). The proposed tool may be useful for the development of a new database of LCI data, as well as to analyze mine’s regional considerations during designs for risk evaluation, management and control prior to development, when data is also scarce. Full article

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems. Full article

Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. Meteorological data for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting may be common for some of these counterintuitive cases. Full article

Introduction. Road safety issues occupy scientists around the world. This study is aimed at creating a comprehensive digital model that will become a tool for developing recommendations for improving road safety in the regions of the Russian Federation. Methods. The assessment of the current state of road safety in the regions of Russia was carried out by means of rating. The object of the study was studied using econometric models, machine learning models, and system dynamics; sensitivity analysis and a balanced scorecard were used. Results. The regions of Russia were divided into three groups according to the level of safety. The econometric model and machine learning model made it possible to assess the influence of independent variables on dependent variables. The identified interrelations formed the basis of a system dynamics model. It was concluded that it is possible to extrapolate the results to groups of regions. For each group of regions, recommendations are given on the formation of a strategy for improving road safety. Conclusions. The practical significance of the study lies in the creation of a tool for the formation of recommendations for the creation of a strategy for improving road safety in the regions of the Russian Federation. Full article

An underground utility tunnel (UUT) is essential for the efficient use of urban underground space. However, current maintenance systems rely on patrol personnel and professional equipment. This study explores industrial detection methods for identifying and monitoring natural gas leaks in UUTs. Via infrared thermal imaging gas experiments, data were acquired and a dataset established. To address the low-resolution problem of existing imaging devices, video super-resolution (VSR) was used to improve the data quality. Based on a convolutional recurrent neural network (CRNN), the image features at each moment were extracted, and the time series data were modeled to realize the risk-level classification mechanism based on the automatic classification of the leakage rate. The experimental results show that when the sliding window size was set to 10 frames, the classification accuracy of the CRNN was the highest, which could reach 0.98. This method improves early warning precision and response efficiency, offering practical technical support for UUT maintenance management. Full article

Series of pyrene-labeled diols (Py₂-DOs) and polyols (Py-POs) were synthesized by coupling a number (n_PyBA) of 1-pyrenebutyric acids to diols and polyols to yield series of end-labeled linear (n_PyBA = 2) and branched (n_PyBA > 2) oligomers, respectively. Pyrene excimer formation (PEF) between an excited and a ground-state pyrene was studied for the Py₂-DO and Py-PO samples by analyzing their fluorescence spectra and decays in tetrahydrofuran, dioxane, N,N-dimethylformamide, and dimethyl sulfoxide. Global model-free analysis (MFA) of the pyrene monomer and excimer fluorescence decays yielded the average rate constant (<k>) for PEF. After the calculation of the local pyrene concentration ([Py]_loc) for the Py₂-DO and Py-PO samples, the <k>-vs.-[Py]_loc plots were linear in each solvent, with larger and smaller slopes for the Py₂-DO and Py-PO samples, respectively, resulting in a clear kink in the middle of the plot. The difference in slope was attributed to a bias for PEF between pyrenes close to one another on the densely branched Py-PO constructs resulting in lower apparent [Py]_loc and <k> values. This study illustrated the ability of PEF to probe how steric hindrance along a main chain affects the dynamic encounters between substituents in multifunctional oligomers such as diols and polyols. Full article

The innovation in thermal storage systems for solar thermal power generation is crucial for achieving efficient utilization of new energy sources. Molten salt has been extensively studied as a phase change material (PCM) for latent heat thermal energy storage systems. In this study, a two-dimensional model of a vertical shell-and-tube heat exchanger is developed, utilizing water-steam as the heat transfer fluid (HTF) and phase change material for heat transfer analysis. Through numerical simulations, we explore the interplay between PCM solidification and HTF boiling. The transient results show that tube length affects water boiling duration and PCM solidification thickness. Higher heat transfer fluid flow rates lower solidified PCM temperatures, while lower heat transfer fluid inlet temperatures delay boiling and shorten durations, forming thicker PCM solidification layers. Adding fins to the tube wall boosts heat transfer efficiency by increasing contact area with the phase change material. This extension of boiling time facilitates greater PCM solidification, although it may not always optimize the alignment of bundles within the thermal energy storage system. Full article

With the increase in the penetration rate of distributed sources and loads, the sensor monitoring data is increasing dramatically. Power grid maintenance services require a rapid response in power quality data analysis. To achieve a rapid response and highly accurate classification of power quality disturbances (PQDs), this paper proposes an efficient classification algorithm for PQDs based on Lissajous trajectory (LT) and a lightweight DenseNet, which utilizes the concept of Lissajous curves to construct an ideal reference signal and combines it with the original PQD signal to synthesize a feature trajectory with a distinctive shape. Meanwhile, to enhance the ability and efficiency of capturing trajectory features, a lightweight L-DenseNet skeleton model is designed, and its feature extraction capability is further improved by integrating an attention mechanism with L-DenseNet. Finally, the LT image is input into the fusion model for training, and PQD classification is achieved using the optimally trained model. The experimental results demonstrate that, compared with current mainstream PQD classification methods, the proposed algorithm not only achieves superior disturbance classification accuracy and noise robustness but also significantly improves response speed in PQD classification tasks through its concise visualization conversion process and lightweight model design. Full article

Background/Objectives: The metastatic patterns of apical lymph node (ALN) in rectal and sigmoid colon cancer are currently unclear, and there is no consensus on the indications for dissection of ALN. This study aimed to analyze the impact of ALN metastasis on prognosis, determine the metastatic patterns of ALN and provide evidence for indications of ALN dissection in rectal and sigmoid colon cancer. Methods: In this multicenter, retrospective cohort study, patients from five centers with stage I-III rectal or sigmoid colon cancer who underwent laparoscopic radical surgery with ALN dissection without neoadjuvant treatment from January 2015 to December 2019 were enrolled. Results: Among 2809 patients, the positive rate of ALN was 1.9%. The 5-year overall survival and cancer-specific survival rate for patients with metastatic ALN were 37.5% and 41.0%, respectively. ALN metastasis was the independent risk factor for poor prognosis. Tumor size ≥5 cm (OR = 2.32, 95% CI: 1.30–4.13, p = 0.004), signet ring cell cancer/mucinous adenocarcinoma (vs. poor differentiated adenocarcinoma, OR = 0.19, 95% CI: 0.08–0.45, p < 0.001; vs. moderate to well differentiated adenocarcinoma, OR = 0.22, 95% CI: 0.11–0.42, p < 0.001), T4 stage (OR = 1.93, 95% CI: 1.05–3.55, p = 0.034), N2 stage (OR = 8.86, 95% CI: 4.45–17.65, p < 0.001) and radiologic evidence of extramural venous invasion (OR = 1.88, 95% CI: 1.03–3.42, p = 0.040) were independent risk factors for ALN metastasis. The nomogram model developed by these factors achieved a good predictive performance. Conclusions: This research offered insights into the incidence, risk factors, and prognostic significance of apical lymph node metastasis in cases of rectal and sigmoid colon cancer. Additionally, the study furnished empirical support for the criteria guiding ALN dissection. Furthermore, a pragmatic risk assessment model was developed to predict ALN metastasis. Full article

There is a present need to develop alternative biotherapeutic drugs to mitigate the exacerbated inflammatory immune responses characteristic of sepsis. The potent endotoxin lipopolysaccharide (LPS), a major component of Gram-negative bacterial outer membrane, activates the immune system via Toll-like receptor 4 (TLR4), triggering macrophages and a persistent cascade of inflammatory mediators. Our previous studies have demonstrated that Fh15, a recombinant member of the Fasciola hepatica fatty acid binding protein family, can significantly increase the survival rate by suppressing many inflammatory mediators induced by LPS in a septic shock mouse model. Although Fh15 has been proposed as a TLR4 antagonist, the specific mechanisms underlying its immunomodulatory effect remained unclear. In the present study, we employed a quantitative proteomics approach using tandem mass tag (TMT) followed by LC-MS/MS analysis to identify and quantify differentially expressed proteins that participate in signaling pathways downstream TLR4 of macrophages, which can be dysregulated by Fh15. Data are available via ProteomeXchange with identifier PXD065520. Based on significant fold change (FC) cut-off of 1.5 and p-value ≤ 0.05 criteria, we focused our attention to 114 proteins that were upregulated by LPS and downregulated by Fh15. From these proteins, TNFα, IL-1α, Lck, NOS2, SOD2 and CD36 were selected for validation by Western blot on murine bone marrow-derived macrophages due to their relevant roles in the NF-κB, iNOS, oxidative stress, and phagosome signaling pathways, which are closely associated with sepsis pathogenesis. These results suggest that Fh15 exerts a broad spectrum of action by simultaneously targeting multiple downstream pathways activated by TLR4, thereby modulating various aspects of the inflammatory responses during sepsis. Full article

This paper explores the complex dynamics of mixed convective flow in a Casson fluid saturated in a non-Darcy porous medium, focusing on the influence of gyrotactic microorganisms, internal heat generation, and multiple convective mechanisms. Casson fluids, known for their non-Newtonian behavior, are relevant in various industrial and biological contexts where traditional fluid models are insufficient. This study addresses the limitations of the standard Darcy’s law by examining non-Darcy flow, which accounts for nonlinear inertial effects in porous media. The governing equations, derived from conservation laws, are transformed into a system of no linear ordinary differential equations (ODEs) using similarity transformations. These ODEs are solved numerically using a finite differencing method that incorporates central differencing, tridiagonal matrix manipulation, and iterative procedures to ensure accuracy across various convective regimes. The reliability of this method is confirmed through validation with the MATLAB (R2024b) bvp4c scheme. The investigation analyzes the impact of key parameters (such as the Casson fluid parameter, Darcy number, Biot numbers, and heat generation) on velocity, temperature, and microorganism concentration profiles. This study reveals that the Casson fluid parameter significantly improves the velocity, concentration, and motile microorganism profiles while decreasing the temperature profile. Additionally, the Biot number is shown to considerably increase the concentration and dispersion of motile microorganisms, as well as the heat transfer rate. The findings provide valuable insights into non-Newtonian fluid behavior in porous environments, with applications in bioengineering, environmental remediation, and energy systems, such as bioreactor design and geothermal energy extraction. Full article