Search Results (4,770)

Sunflower (Helianthus annuus L.) is a cross-pollinated species that relies on pollinators, attracted by itsnectar composition. Nectar consists primarily of sugars (up to 70%), with sucrose, glucose, and fructose being dominant, while minor components such as mannose, arabinose, xylose, and sugar alcohols (e.g., mannitol and inositol) occur in lower concentrations and vary with biotic and abiotic factors. This study developed a robust high-performance liquid chromatography method with refractive index detection (HPLC-RID) for the simultaneous quantification of eight sugars (D-ribose, xylose, arabinose, fructose, mannose, glucose, sucrose, and maltose) and two sugar alcohols (mannitol, meso-inositol) in wild sunflower nectar. A Box–Behnken design (BBD), coupled with response surface methodology (RSM), was used to systematically optimize column temperature (20–23 °C), acetonitrile concentration (80–85%), and flow rate (0.7–1 mL/min), while achieving baseline separation of critical sugar pairs, including the previously co-eluting glucose/mannitol and glucose/mannose. Satisfactory resolution (Rs > 1 for all analytes) was achieved under optimized separation conditions comprising a column temperature of 20 °C, 82.5% acetonitrile, and a flow rate of 0.766 mL/min. The RSM efficiently evaluated factor interactions to maximize chromatographic performance, resulting in an optimized protocol that provides a cost-effective and environmentally friendly alternative to conventional sugar analysis methods. Method validation confirmed satisfactory linearity across relevant concentration ranges (50–500 mg/L for most sugars; 50–5500 mg/L for fructose and glucose), with correlation coefficients (R) between 0.985 and 0.999. The limits of detection (LOD) and quantification (LOQ) for the analyzed sugars and sugar alcohols ranged from 4.04 to 19.46 mg/L and from 13.46 to 194.61 mg/L, respectively. Glucose exhibited the highest sensitivity showing LOD of 4.04 and LOQ of 13.46 mg/L, whereas mannose was identified as the least sensitive analyte, with LOD of 19.46 mg/L and LOQ of 194.61 mg/L. The described method represents a reliable tool for sugar and sugar alcohol analysis in sunflower nectar and can be extended to other plant and food matrices with suitable sample preparation. Full article

Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

During machining, most of the mechanical energy is converted into heat. A substantial part of this heat is transferred to the cutting tool, causing a rapid rise in tool temperature. Excessive thermal loads accelerate tool wear and lead to displacement of the tool center point, reducing machining accuracy and workpiece quality. This challenge is particularly pronounced when machining titanium alloys. Due to their low thermal conductivity, titanium alloys impose significantly higher thermal loads on the cutting tool compared to conventional carbon steels, making the process more difficult. To reduce temperatures in the cutting zone, cutting fluids are widely employed in titanium machining. They have been shown to significantly extend tool life. Cutting fluids are broadly categorized into cutting oils and water-based cutting fluids. Owing to their distinct thermophysical properties, these fluids exhibit notably different cooling and lubrication performance. However, current research lacks comprehensive cross-comparative studies of different cutting fluid types, which hinders the selection of optimal cutting fluids for process optimization. This study examines the influence of three cutting fluids—emulsion, cutting oil, and synthetic oil-free fluid—on tool wear, temperature, surface quality, and energy consumption during flood-cooled end milling of Ti-6Al-4V. A novel experimental setup incorporating embedded thermocouples enabled real-time temperature measurement near the cutting edge. Tool wear, torque, and surface roughness were recorded over defined feed lengths. Among the tested fluids, emulsion achieved the best balance of cooling and lubrication, resulting in the longest tool life with a feed travel path of 12.21 m. This corresponds to an increase of approximately 200 % compared to cutting oil and oil-free fluid. Cutting oil offered superior lubrication but limited cooling capacity, resulting in localized thermal damage and edge chipping. Water-based cutting fluids reduced tool temperatures by over 300 ${}^{\circ }$C compared to dry cutting but, in some cases, increased notch wear due to higher mechanical stress at the entry point. Power consumption analysis revealed that the cutting fluid supply system accounted for 60–70 % of total energy use, particularly with high-viscosity fluids like cutting oil. Complementary thermal and CFD simulations were used to quantify heat partitioning and convective cooling efficiency. The results showed that water-based fluids achieved heat transfer coefficients up to 175 kW/m²· K, more than ten times higher than those of cutting oil. These findings emphasize the importance of selecting suitable cutting fluids and optimizing their supply to enhance tool performance and energy efficiency in Ti-6Al-4V machining. Full article

Fiber-reinforced composites are widely used in aerospace and other fields due to their excellent specific strength, specific stiffness, and corrosion resistance, and further study of their failure criteria is essential to improve the accuracy and reliability of failure behavior prediction under complex loads. There are still some limitations in the current composite failure criterion research, mainly reflected in the lack of promotion of three-dimensional stress state, lack of sufficient consideration of multi-modal coupling effects, and the applicability of the criteria under multiaxial stress and complex loading conditions, which limit the wider application of composites in the leading-edge fields to a certain degree. In this work, a generalized Mohr failure envelope function approach is adopted to obtain the stress on the failure surface as a power series form of independent variable, and the unknown coefficients are determined according to the damage conditions, to extend the Chang–Chang criterion to the three-dimensional stress state, and to consider the coupling effect between the fiber and matrix failure modes. The modified Chang–Chang criterion significantly enhances the failure prediction accuracy of composite materials under complex stress states, especially in the range of multi-axial loading and small off-axis angles, which provides a more reliable theoretical basis and practical guidance for the safe design and performance optimization of composite structures in aerospace and other engineering fields. Full article

This study considers a robust design methodology to reduce cogging torque in the EPS (Electric Power Steering) of an automotive system. Cogging torque reduction is the key design factor to improve steering feeling and drive stability in an EPS system. For this reason, an SPMSM (Surface Permanent Magnet Synchronous Motor) has been widely applied to drive a motor in an EPS system. Furthermore, two design methods, which are a magnet shape and step-skew design for rotor assembly, have been mainly used to reduce cogging torque in an SPMSM. In this paper, an SPMSM is selected as the drive motor and a robust design methodology is proposed to reduce cogging torque in an EPS system. Firstly, a cycloid curve is used for the magnet shape to reduce cogging torque. An evaluation index δq is also used to compare this with a conventional magnet shape design. Secondly, based on the results of the magnet shape design with the cycloid curve, a step-skew design for rotor assembly is also applied to reduce cogging torque. In order to validate the effectiveness of the robust design for the cycloid curve and conventional magnet shape with rotor step-skew, the results from FEM (Finite Element Method) analysis and prototype tests are compared. The cycloid curve magnet shape model with rotor step-skew was verified to reduce the cogging torque and enhance the robustness for cogging torque variation through the analysis and protype test results. The verified results for the proposed model will be extended to meet the required cogging torque variation for the various applications driven by SPMSM with the robust design model. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

The mechanical and structural design of railway vehicles is heavily influenced by their lifetime. Because fatigue is a significant factor that impacts the longevity of railway components, it is imperative that the fatigue resistance properties of crucial components, like leaf springs, be thoroughly investigated. This research investigates the fatigue resistance of 51CrV4 steel under bending and axial tension, considering different stress ratios across low-cycle fatigue (LCF), high-cycle fatigue (HCF), and very-high-cycle fatigue (VHCF) regimes, using experimental data collected from this work and prior research. Data included fractographic analyses aiming to help in understanding some of failures for different loads. The presence of geometric discontinuities, such as notches, amplifies stress concentrations, requiring a probabilistic approach to fatigue assessment. To address notch effects, the theory of critical distances (TCD) was employed to evaluate fatigue strength. TCD model was integrated in fatigue statistical models, such as the Walker model (WSN) and the Castillo–Fernández-Cantelli model adapted for mean stress effects (ACFC). Extending the application of the TCD theory, this research provides an improved probabilistic fatigue model that integrates notch sensitivity, mean stress effects, and fatigue regimes, contributing to more reliable design approaches of railway leaf springs or other components produced in 51CrV4 steel. Full article

Plums are climacteric fruits with a short postharvest shelf-life, which makes them highly susceptible to spoilage by moulds and pathogens. Biological control using antagonistic yeasts offers a promising approach to extend shelf-life by inhibiting fungal growth. This study evaluated the effects of two yeast strains, Hanseniaspora uvarum L793 and Metschnikowia pulcherrima L672, on the quality of ‘Larry Ann’ Japanese plums during cold storage. Plums were divided into three batches: two treated by immersion in yeast suspensions (10⁸ cells mL⁻¹) and one untreated control. Quality parameters assessed over 12 weeks at 1 °C included weight loss, decay index, microbial counts, yeast colonisation, skin and flesh colour, texture, pH, titratable acidity, total soluble solids, and ripening index, with evaluations every week. M. pulcherrima L672 showed strong colonisation and persistence on the plum surface, significantly reducing skin damage and mould incidence. In contrast, H. uvarum L793 initially colonised well but declined over time, being replaced by native yeasts such as Aureobasidium spp. Both treatments maintained the physicochemical and organoleptic quality of the plums throughout storage. However, M. pulcherrima L672 was more effective in suppressing fungal growth and preserving fruit integrity. These findings suggest that M. pulcherrima L672 is a promising biocontrol agent for prolonging the shelf-life of Japanese plums during cold storage, maintaining their commercial quality for up to three months. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

Structure and nonlinear spectra of the basal surface of ice Ih were investigated by molecular dynamics simulations. At a temperature significantly lower than the melting temperature $T_m$, the ice structure at the interface is only weakly perturbed by the presence of surface. The computed nonlinear spectrum of the interface well agrees with the experimental data and the results of the calculations provide the molecular-level interpretation of spectral features. In particular, the ice surface specific positive peaks in the Im[${\chi }^{\left(2\right)}$] spectrum at ∼3180 cm⁻¹ and at ∼3420 cm⁻¹ were found to result from the low- and high-frequency vibrational modes of quadruply H-bonded surface molecules, respectively. The spectrum of the crystalline ice interface is significantly affected by intermolecular interactions. Upon increasing the temperature, the structural disorder extends to the second water bilayer. The thickness of the premelted water layer of 6–8 Å can be estimated at the temperature by ca. 5 K below $T_m$. The increase in the temperature results in a change in the intensity and shape of the nonlinear spectrum of the ice Ih interface. The changes can be explained by the interconversion between different H-bonded surface species and by an increase in disordering of water molecules that reduces strength of intermolecular interactions. Results of the present work contribute to our understanding of the structure–spectrum relationship of the ice/air interface, and shed light on the origins of features in the nonlinear spectra of the system. Full article

This paper presents a new approach for providing contextual information in real-world environments. Our approach is consciously designed to be low-threshold; by using mirrors as augmented reality surfaces, no devices such as AR glasses or smartphones have to be worn or held by the user. It enables technical and non-technical objects in the environment to be visually highlighted and thus subtly draw the attention of people passing by. The presented technology enables the provision of information that can be viewed in more detail by the user if required by slowing down their movement. Users can decide whether this is relevant to them or not. A prototype system was implemented and evaluated through a user study. The results show a high level of acceptance and intuitive usability of the system, with participants being able to reliably perceive and process the information displayed. The technology thus offers promising potential for the unobtrusive and context-sensitive provision of information in various application areas. The paper discusses limitations of the system and outlines future research directions to further optimize the technology and extend its applicability. Full article

In China, most of the ancient wooden structure mortise and tenon buildings, under the long-term upper load, have columns with surface surfaces that have varying degrees of damage, which need to be repaired and strengthened urgently, but the theory related to CFRP, mortise size, and pre-damage simulation still needs to be deeply studied. To investigate the effects of CFRP reinforcement layers, cross-sectional area of concealed tenons as the projected area after installation, and tenon engagement length as the axial length after installation on the axial compressive mechanical properties of pre-damaged quad-segment spliced Pinus sylvestris var. mongolia composited wooden columns, axial compression failure tests were conducted on 10 specimens following pre-damage simulation and CFRP strengthening. The experimental program yielded comprehensive data, including observations, mechanical analyses, load-displacement curves, load-strain curves, ultimate load-bearing capacities, ductility coefficients, and stiffness values. The results demonstrate that with consistent tenon cross-sectional area and engagement length, increasing CFRP layers from 1 to 3 raises the ultimate bearing capacity from 472.3 kN to 620.3 kN and improves the ductility coefficient from 4.67 to 7.95, clearly indicating that CFRP reinforcement significantly enhances axial compressive performance while effectively mitigating brittle failure. When maintaining constant CFRP layers and tenon cross-sectional area, extending the tenon engagement length from 30 mm to 90 mm elevates the bearing capacity from 494.95 kN to 546.3 kN and boosts the ductility coefficient from 5.58 to 7.95. In contrast, with fixed CFRP layers and engagement length, expanding the tenon cross-sectional area from 360 mm² to 810 mm² produces only marginal bearing capacity improvement from 548.2 kN to 556.2 kN with ductility coefficients ranging between 4.67 and 5.56, conclusively revealing that tenon engagement length has substantially greater influence on mechanical properties than cross-sectional area. The optimal axial compressive performance configuration combines 3 CFRP layers, an 810 mm² tenon cross-section, and a 90 mm engagement length. Full article

Molecular dynamics (MD) simulations provide atomistic insights into nucleation and crystallization in polymers, yet interpreting their complex spatiotemporal data remains a challenge. Existing order parameters face limitations, such as failing to account for directional alignment or lacking sufficient spatial resolution, preventing them from accurately capturing the anisotropic and heterogeneous characteristics of nucleation or the surface phenomena of polymer crystallization. We introduce a novel set of local order parameters—namely, directional entropy bands— that extend scalar entropy-based descriptors by capturing first-order angular moments of the local entropy field around each particle. We compare these against conventional metrics (entropy, the crystallinity index, and smooth overlap of atomic positions (SOAP) descriptors) in equilibrium MD simulations of polymer crystallization. We show that (i) scalar entropy bands demonstrate advantages compared to SOAP in polymer phase separation at single-snapshot resolution and (ii) directional extensions (dipole projections and gradient estimates) robustly highlight the evolving crystal–melt interface, enabling earlier nucleation detection and quantitative surface profiling. UMAP embeddings of these 24–30D feature vectors reveal a continuous melt–surface–core manifold, as confirmed by supervised boundary classification. Our approach is efficient and directly interpretable, offering a practical framework for studying polymer crystallization kinetics and surface growth phenomena. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Tea tree seeds are highly sensitive to dehydration and cannot be stored for extended periods, making surface defect detection crucial for preserving their germination rate and overall quality. To address this challenge, we propose Cgc-YOLO, an enhanced YOLO-based model specifically designed to detect small-scale and complex surface defects in tea seeds. A high-resolution imaging system was employed to construct a dataset encompassing five common types of tea tree seeds, capturing diverse defect patterns. Cgc-YOLO incorporates two key improvements: (1) GhostBlock, derived from GhostNetV2, embedded in the Backbone to enhance computational efficiency and long-range feature extraction; and (2) the CPCA attention mechanism, integrated into the Neck, to improve sensitivity to local textures and boundary details, thereby boosting segmentation and localization accuracy. Experimental results demonstrate that Cgc-YOLO achieves 97.6% mAP50 and 94.9% mAP50–95, surpassing YOLO11 by 2.3% and 3.1%, respectively. Furthermore, the model retains a compact size of only 8.5 MB, delivering an excellent balance between accuracy and efficiency. This study presents a robust and lightweight solution for nondestructive detection of tea seed defects, contributing to intelligent seed screening and storage quality assurance. Full article