Search Results (32,022)

Under conservation tillage in the Huang-Huai-Hai wheat–maize rotation area, the ridge-clearing no-till seeder for strip tillage mitigates the adverse impacts of surface residues on seeding quality by clearing stubble specifically within the seed rows, demonstrating significant potential for application and promotion. However, the inadequate understanding of the seeder’s operational performance and governing mechanisms under varying field conditions hinders its high-quality and efficient implementation. To address this issue, this study selected the stubble height, forward speed, and stubble knife rotational speed (PTO speed) as experimental factors. Employing a three-factor quasi-level orthogonal experimental design, coupled with response surface regression analysis, this research systematically elucidated the interaction mechanisms among these factors concerning the seeding depth consistency and seed spacing uniformity of the seeder. An optimized parameter-matching model was subsequently derived through equation system solving. Field trials demonstrated that a lower forward speed improved the seed spacing uniformity and seeding depth consistency, whereas high speeds increased the missing rates and spacing deviations. An appropriate stubble height enhanced the seed spacing accuracy, but an excessive height compromised depth precision. Higher PTO speeds reduced multiple indices but impaired depth accuracy. Response surface analysis based on the regression models demonstrated that the peak value of the seed spacing qualification index occurred within the forward speed range of 8–9 km/h and the stubble height range of 280–330 mm, with the stubble height being the dominant factor. Similarly, the peak value of the seeding depth qualification index occurred within the stubble height range of 300–350 mm and the forward speed range of 7.5–9 km/h, with the forward speed as the primary factor. Validation confirmed that combining stubble heights of 300−330 mm, forward speeds of 8−9 km/h, and PTO speeds of 540 r/min optimized both metrics. This research reveals nonlinear coupling relationships between operational parameters and seeding quality metrics, establishes a stubble–speed dynamic matching model, and provides a theoretical foundation for the intelligent control of seeders in conservation tillage systems. Full article

In this study, we aimed to address the lack of systematic research on key collision dynamics parameters (elastic restitution coefficient) in the full mechanization of rapeseed operations, which hinders the development of precision agriculture. In this present work, the restitution coefficient of rapeseed was systematically investigated, and a predictive model (R² = 0.959) was also established by using Box–Behnken design response surface methodology (BBD-RSM). The results show that the collision restitution coefficient varies in the range of 0.539–0.649, with the key influencing factors ranked as follows: moisture content (M_c) > material layer thickness (L) > drop height (H). The EDEM simulation methodology was adopted to validate the experimental results, and the results show that there is a minimal relative error (−1% < δ < 1%) between the measured and simulated rebound heights, indicating that the established model shows a reliable prediction performance. Moreover, by comprehensively analyzing stress, strain, and energy during the collision process between rapeseed and Q235 steel, it can be concluded that the process can be divided into five stages—free fall, collision compression, collision recovery, rebound oscillation, and rebound stabilization. The maximum stress (1.19 × 10⁻² MPa) and strain (6.43 × 10⁻⁶ mm) were observed at the beginning of the collision recovery stage, which can provide some theoretical and practical basis for optimizing and designing rapeseed machines, thus achieving the goals of precise control, harvest loss reduction, and increased yields. Full article

While boreal forests constitute 30% of the Earth’s forested area and are responsible for 20% of the global carbon sink, there is considerable concern about their sustainability. This paper focuses on the role of elevated CO₂, examining whether wood volume in these forests has responded to increased CO₂ over the last 60 years. To accomplish this, we use a rich set of wood volume measurement data from the Province of Alberta, Canada, and deploy quasi-experimental techniques to determine the effect of elevated CO₂. While the few experimental studies that have examined boreal forests have found almost no effect of elevated CO₂, our results indicate that a 1.0% increase in lifetime exposure to CO₂ leads to a 1.1% increase in aboveground wood volume in these boreal forests. This study showcases the value of research designs that use natural settings to better account for the effects of prolonged exposure to elevated CO₂. Our results should enable improved delineation of the drivers of historical changes in wood volume and carbon storage in boreal forests. In addition, when combined with other studies, these results will likely aid policymakers in designing management or policy approaches that will enhance the sustainability of forests in boreal regions. Full article

Electromagnetic and magnetic devices are increasingly prevalent in sectors such as transportation, industry, and renewable energy due to the ongoing electrification trend. These devices exhibit nonlinear behavior, particularly under signals rich in harmonics. They require precise and appropriate modeling for accurate sizing. Identifying model-specific parameters, which depend on frequency, is crucial. This article focuses on a specific frequency range where a circuit model with series resistance and inductance, along with a parallel resistance to account for iron losses (${\mathrm{R}}_{\mathrm{iron}}$), is applicable. While the determination of series elements is well documented, the determination of ${\mathrm{R}}_{\mathrm{iron}}$ remains complex and debated, with traditional methods neglecting operating conditions such as magnetic saturation. To address these limitations, an innovative experimental method is proposed, comprising two main steps: determining the complex impedance of the magnetic device and extracting ${\mathrm{R}}_{\mathrm{iron}}$ from the model. This method aims to provide a more precise and representative estimation of ${\mathrm{R}}_{\mathrm{iron}}$, improving the reliability and accuracy of electromagnetic and magnetic device simulations and designs. The obtained values of the iron loss equivalent resistance are different by at least 300% than those obtained by an impedance analyzer. The proposed method is expected to advance the understanding and modeling of losses in electromagnetic and magnetic devices, offering more robust tools for engineers and researchers in optimizing device performance and efficiency. Full article

Analyzing the two-phase flow behavior in bearing lubrication is crucial for understanding friction and wear mechanisms, optimizing lubrication design, and improving bearing operational efficiency and reliability. However, the complexity of oil–air two-phase flow in high-speed bearings poses significant research challenges. Currently, there is a lack of comparative studies employing different simulation strategies to address this issue, leaving a gap in evidence-based guidance for selecting appropriate simulation approaches in practical applications. This study begins with a comparative analysis between experimental and simulation results to validate the reliability of the adopted simulation approach. Subsequently, a comparative evaluation of different simulation methods is conducted to provide a scientific basis for relevant decision-making. Evaluated from three dimensions—adaptability to rotational speed conditions, research focuses (oil distribution and power loss), and computational economy—the findings reveal that FVM excels at medium-to-high speeds, accurately predicting continuous oil film distribution and power loss, while MPS, leveraging its meshless Lagrangian characteristics, demonstrates superior capability in describing physical phenomena under extreme conditions, albeit with higher computational costs. Economically, FVM, supported by mature software ecosystems and parallel computing optimization, is more suitable for industrial design applications, whereas MPS, being more reliant on high-performance hardware, is better suited for academic research and customized scenarios. The study further proposes that future research could adopt an FVM-MPS coupled approach to balance efficiency and precision, offering a new paradigm for multi-scale lubrication analysis in bearings. Full article

Physical activity, particularly when practiced in natural settings, has well-established benefits for overall health, sleep, and body composition. These effects are especially important for postmenopausal women, although research specifically targeting this population remains limited. The study evaluated a 16-week multicomponent outdoor exercise program (cardiorespiratory, strength, balance, coordination, and flexibility training) in postmenopausal women, consisting of three 60 min sessions per week. Participants were non-randomly assigned to an experimental group (EG, n = 55) and a control group (CG, n = 20). Measurements were taken at baseline and after 16 weeks, including body composition, sleep (duration and quality), and connection with nature. No significant differences were observed between groups at baseline. After the intervention, the EG and CG presented significant differences (p ≤ 0.01) in the rates of change in body mass, fat mass (FM; −9.26% and −1.21%, respectively), and visceral fat level (VFL; −13.46 points and −3.80 points). These differences were also observed for the sleep fragmentation index (p ≤ 0.01), but not for connection with nature. A significant interaction effect (p < 0.01) of time × group was observed for %FM, VFL, and appendicular skeletal muscle mass. Exercise duration had an effect (p = 0.043) on participants’ personal and affective identification with nature, and the time × group × medication interaction significantly influenced sleep efficiency (p = 0.034). The exercise program proved effective in reducing total and central adiposity levels; however, it did not lead to improvements in sleep duration, sleep quality, or connection with nature. Full article

This study presents a novel method for controlling the position and orientation of the bioinspired I-SUPPORT soft robot, which represents a relevant advancement in the field of soft robotics. The approach is based on module actuation decoupling and fractional-order control, offering a more advanced and robust control solution. This innovation enhances the versatility of the robot and illustrates the efficacy of fractional-order controllers, which are comparable to current meta-learning-based controllers. The research involves experiments in both vertical and horizontal configurations, addressing tasks ranging from simple orientation to complex interactions, such as gentle rubbing during bathing activities with the robot. These experimental results exemplify the efficacy of the proposed control strategy and provide a foundation for future research in soft robotics control, underscoring its potential for broader applications and further technological advancement. Full article

High-strength steel has high strength and low thermal conductivity, and its thin-walled parts are very susceptible to residual stress and deformation caused by cutting heat during the drilling process, which affects the machining accuracy and quality. High-strength steel thin-walled components are widely used in aerospace and other high-end sectors; however, systematic investigations into their temperature fields during drilling remain scarce, particularly regarding the evolution characteristics of the temperature field in thin-wall drilling and the quantitative relationship between drilling parameters and these temperature variations. This paper takes the thin-walled parts of AF1410 high-strength steel as the research object, designs a special fixture, and applies infrared thermography to measure the bottom surface temperature in the thin-walled drilling process in real time; this is carried out in order to study the characteristics of the temperature field during the thin-walled drilling process of high-strength steel, as well as the influence of the drilling dosage on the temperature field of the bottom surface. The experimental findings are as follows: in the process of thin-wall drilling of high-strength steel, the temperature field of the bottom surface of the workpiece shows an obvious temperature gradient distribution; before the formation of the drill cap, the highest temperature of the bottom surface of the workpiece is distributed in the central circular area corresponding to the extrusion of the transverse edge during the drilling process, and the highest temperature of the bottom surface can be approximated as the temperature of the extrusion friction zone between the top edge of the drill and the workpiece when the top edge of the drill bit drills to a position close to the bottom surface of the workpiece and increases with the increase in the drilling speed and the feed volume; during the process of drilling, the highest temperature of the bottom surface of the workpiece is approximated as the temperature of the top edge of the drill bit and the workpiece. The maximum temperature of the bottom surface of the workpiece in the drilling process increases nearly linearly with the drilling of the drill, and the slope of the maximum temperature increases nearly linearly with the increase in the drilling speed and feed, in which the influence of the feed on the slope of the maximum temperature increases is larger than that of the drilling speed. Full article

In response to the problem of complex and variable loads on wind turbine gearbox bearing in working conditions, as well as the limited amount of sound data making fault identification difficult, this study focuses on sound signals and proposes an intelligent diagnostic method using deep learning. By adding the CBAM module in ResNeXt to enhance the model’s attention to important features and combining it with the Arcloss loss function to make the model learn more discriminative features, the generalization ability of the model is strengthened. We used a fine-tuning transfer learning strategy, transferring pre-trained model parameters to the CBAM-ResNeXt50-ArcLoss model and training with an extracted Mel spectrogram of sound signals to extract and classify audio features of the wind turbine gearbox. Experimental validation of the proposed method on collected sound signals showed its effectiveness and superiority. Compared to CNN, ResNet50, ResNeXt50, and CBAM-ResNet50 methods, the CBAM-ResNeXt50-ArcLoss model achieved improvements of 13.3, 3.6, 2.4, and 1.3, respectively. Through comparison with classical algorithms, we demonstrated that the research method proposed in this study exhibits better diagnostic capability in classifying wind turbine gearbox sound signals. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness. Full article

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

In recent years, biofuels and bioenergy derived from algae have gained increasing attention, fueled by the growing demand for renewable energy sources and the urgent need to lower CO₂ emissions. This research examines the generation of bioethanol and biomethane using freshly harvested and sedimented algal biomass. Employing a factorial experimental design, various trials were conducted, with ethanol yield as the primary optimization target. The findings indicated that the sodium hydroxide concentration during pretreatment and the amylase dosage in enzymatic hydrolysis were key parameters influencing the ethanol production efficiency. Under optimized conditions—using 0.3 M NaOH, 25 μL/g starch, and 250 μL/g cellulose—fermentation yielded ethanol concentrations as high as 2.75 ± 0.18 g/L (45.13 ± 2.90%), underscoring the significance of both enzyme loading and alkali treatment. Biomethane potential tests on the residues of fermentation revealed reduced methane yields in comparison with the raw algal feedstock, with a peak value of 198.50 ± 25.57 mL/g volatile solids. The integrated process resulted in a total energy recovery of up to 809.58 kWh per tonne of algal biomass, with biomethane accounting for 87.16% of the total energy output. However, the energy recovered from unprocessed biomass alone was nearly double, indicating a trade-off between sequential valorization steps. A comparison between fresh and dried feedstocks also demonstrated marked differences, largely due to variations in moisture content and biomass composition. Overall, this study highlights the promise of integrated algal biomass utilization as a viable and energy-efficient route for sustainable biofuel production. Full article

Originally introduced in the 1960s by DISA Elektronik as a calibration tunnel for hot-wire anemometers, the Type 55D41 has now been reengineered into a versatile and modern aerodynamic test platform. While retaining key legacy components, such as the converging nozzle and the 55D42 power unit, the upgraded system features a redesigned modular test section with optical-grade quartz windows. This enhancement enables compatibility with advanced flow diagnostics and visualization methods, including PTV, DIC, and schlieren imaging. The modernized facility maintains the precision and flow stability that made the original design widely respected, while expanding its functionality to meet the demands of contemporary experimental research. Its architecture supports the aerodynamic characterization of micro-scale static pressure probes used in aerospace, propulsion, and micro gas turbine applications. Special attention is given to assessing the influence of probe tip geometry (e.g., conical, ogive), port positioning, and stem interference on measurement accuracy. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article