Search Results (263)

Compression–ignition engines emit particulate matter (PM) (soot), prompting the widespread use of diesel particulate filters (DPFs) in the automotive sector. An alternative method for PM reduction involves the use of ultrasonic waves to disperse and modify the structure of exhaust particles. This article presents experimental results of the effects of ultrasonic emitter parameters, including the number, arrangement, and power, along with the engine speed, on the exhaust smoke density. Tests were conducted on a laboratory prototype equipped with six ultrasonic emitters spaced 0.17 m apart. The exhaust source was a diesel engine from a construction excavator, based on the MTZ-80 tractor design, delivering 80 HP and a displacement of 4750 cm³. A regression model was developed to describe the relationship between the engine speed, emitter power and spacing, and smoke density. The optimal configuration was found to involve an emitter power of 319.35 W and a spacing of 1.361 m for a given engine speed. Under the most effective conditions—an engine speed of 1500 rpm, six active emitters, and a total power of 600 W—smoke emissions were reduced by 18%. These findings support the feasibility of using ultrasonic methods as complementary or alternative exhaust gas filtration techniques for non-road diesel engines. Full article

When hypersonic vehicles fly in near space, the flow field near the optical window leads to light displacement, jitter, blurring, and energy attenuation of the star sensor. This ultimately affects the imaging quality and navigation accuracy. In order to investigate the impact of aerodynamic optical effects on imaging, the fourth-order Runge–Kutta and the fourth-order Adams–Bashforth–Moulton (ABM) predictor-corrector methods are used for ray tracing on the density data. A comparative analysis of the imaging quality results from the two methods reveals their respective strengths and limitations. The influence of the optical system is included in the image quality calculations to make the results more representative of real data. The effects of altitude, velocity, and angle of attack on the imaging quality are explored when the optical window is located at the tail of the vehicle. The results show that altitude significantly affects imaging results, and higher altitudes reduce the impact of the flow field on imaging quality. When the optical window is located at the tail of the vehicle, the relationship between velocity and offset is no longer simply linear. This research provides theoretical support for analyzing the imaging quality and navigation accuracy of a star sensor when a vehicle is flying at hypersonic speeds in near space. Full article

This article reviews and discusses the behaviours and patterns associated with panic evacuations, as documented in the literature, which must be considered when analysing and modelling such events. This article does not take the form of a typical research article but, rather, a review of previous studies alongside its own commentary. The studies analysed in this article were selected according their ability to provide a new perspective. Where possible, diverse perspectives from existing research have been contrasted with the author’s own observations and reflections. Structured as an overview, this article introduces subsequent analyses and highlights several non-intuitive questions that arose during the investigation. This study examines the relationship between movement velocity and crowd density, comparing experimental data with simulations conducted to date. It also explores the connections between flow rate, crowd density, and velocity and suggests potential directions for further research in this field. Additionally, this article addresses the loss of evacuation coordination under crowding conditions and presents studies that demonstrate optimal evacuation at speeds that differ from the so-called comfortable pace. The positive effects of strategically placed obstacles in reducing congestion and improving evacuation times are also analysed. This literature review is conducted from a practical perspective, with the primary aim of deepening our understanding of panic evacuation phenomena. Furthermore, this article categorises the impact of various phenomena associated with stampedes and panic evacuations on the requirements for safe evacuation. A tabular summary of the technical and structural measures for evacuation is provided, which may prove useful in designing effective evacuation strategies when dealing with heightened emotional states among evacuees. Full article

To investigate the relationship between the seeding performance of a novel pre-cut sugarcane planter designed by South China Agricultural University and operational settings, field seeding tests was conducted with the following protocol: First, the John Deere M1654 tractor’s forward velocity was calibrated, and the planter’s safe loading capacity was determined. Subsequently, eight experimental treatments (A–H) were designed to quantify the relationships between the three performance indicators: seeding density N, the seeding efficiency E and seeding uniformity (coefficient of variation, CV), and three key operational parameters: forward speed of planter v, the discharging sprocket rotational speed n, and the hopper outlet size w. Mathematical models (R20.979) between three key operational parameters with two performance indicators (N, E) was developed through analysis of variance (ANOVA) and regression analysis. The seeding rate per meter was confirmed to follow a Poisson distribution based on Kolmogorov–Smirnov (K–S) tests. When the CV was below 40%, the mean relative error remained within 3%. These findings provide a theoretical foundation for seeding performance prediction under field conditions. Full article

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications. Full article

As small-sized targets, cotton top buds pose challenges for traditional full-image search methods, leading to high sparsity in the feature matrix and resulting in problems such as slow detection speeds and wasted computational resources. Therefore, it is difficult to meet the dual requirements of real-time performance and accuracy for field automatic topping operations. To address the low feature density and redundant information in traditional full-image search methods for small cotton top buds, this study proposes LGN-YOLO, a leaf-morphology-based region-of-interest (ROI) generation network based on an improved version of YOLOv11n. The network leverages young-leaf features around top buds to determine their approximate distribution area and integrates linear programming in the detection head to model the spatial relationship between young leaves and top buds. Experiments show that it achieves a detection accuracy of over 90% for young cotton leaves in the field and can accurately identify the morphology of young leaves. The ROI generation accuracy reaches 63.7%, and the search range compression ratio exceeds 90%, suggesting that the model possesses a strong capability to integrate target features and that the output ROI retains relatively complete top-bud feature information. The ROI generation speed reaches 138.2 frames per second, meeting the real-time requirements of automated topping equipment. Using the ROI output by this method as the detection region can address the problem of feature sparsity in small targets during traditional detection, achieve pre-detection region optimization, and thus reduce the cost of mining detailed features. Full article

Starch is one of the leading nutritional carbohydrates in the human diet; its characteristics, such as digestion rate, depend on molecular structure, and in particular, the molecular composition, type and length of amylopectin chains, which are known to present a parabolic behavior with respect to digestion rate. Amylopectin with a higher density of small branches (Chains A) and those abundant in long chains (B2/B3) often present a marked resistance to digestion and could be a challenge in bread production since both fermentation and digestion could be further modulated in the presence of hydrocolloids or gluten. The objective of this work was to analyze different mixtures of starches (rice, potato, and corn) with hydrocolloids (guar and xanthan gum) and vital gluten to understand the relationship between chain length and molecular characteristics with respect to speed of digestion and glycemic index, and their incorporation into a bread loaf at 50 and 100% wheat flour substitution. A Plackett–Burman design was used to design the mixtures. Mixtures were characterized in terms of amylose/amylopectin content, fast, slow, and resistant (SDS, RS) starch digestion fractions, in vitro glycemic index, molecular weight (Mw), radius of gyration (Rz) of amylopectin, chain length distribution, and textural analysis. In the bread, a tendency to increase the SDS was observed when the mixtures included rice or potato, which can be related to the relationship between Mw and size and the prevalence of B2 and B3 chains. The Rz and RS content were related to average chain size and amylose content. The use of vital gluten was a determinant in achieving volume and textural characteristics in the final products and significantly affected the proportions of SDS and RS. By combining the molecular characteristics of starch with hydrocolloids, we can obtain food ingredients for specific applications, such as gluten-free products. Full article

Additive manufacturing (AM) is gaining widespread adoption in the manufacturing industry due to its capability to fabricate intricate and high-performance components. In parallel, the increasing emphasis on functional materials in AM has highlighted the critical need for accurate prediction of the mechanical behavior of composite systems. This study experimentally investigates the tensile strength and surface quality of carbon fiber-reinforced nylon composites (PA12-CF) fabricated via fused deposition modeling (FDM) and models their behavior using artificial neural networks (ANNs). A Taguchi L27 orthogonal array was employed to design experiments involving five critical printing parameters: layer thickness (100, 200, and 300 µm), infill pattern (gyroid, honeycomb, and triangles), nozzle temperature (250, 270, and 290 °C), printing speed (50, 100, and 150 mm/s), and infill density (30, 60, and 90%). An analysis of variance (ANOVA) revealed that the infill density had the most significant influence on the resulting tensile strength, contributing 53.47% of the variation, with the strength increasing substantially at higher densities. In contrast, the layer thickness was the dominant factor in determining surface roughness, accounting for 53.84% of the variation, with thinner layers yielding smoother surfaces. Mechanistically, a higher infill density enhances the internal structural integrity of the parts, leading to an improved load-bearing capacity, while thinner layers improve the interlayer adhesion and surface finish. The highest tensile strength achieved was 69.65 MPa, and the lowest surface roughness recorded was 9.18 µm. An ANN model was developed to predict both the tensile strength and surface roughness based on the input parameters. Its performance was compared with that of three other machine learning (ML) algorithms: support vector regression (SVR), random forest regression (RFR), and XGBoost. The ANN model exhibited superior predictive accuracy, with a coefficient of determination (R² > 0.9912) and a mean validation error below 0.41% for both outputs. These findings demonstrate the effectiveness of ANNs in modeling the complex relationships between FDM parameters and composite properties and highlight the significant potential of integrating ML and statistical analysis to optimize the design and manufacturing of high-performance AM fiber-reinforced composites. Full article

Tropical cyclone (TC) track clustering plays a crucial role in understanding cyclone movement patterns, which is essential for risk assessment and disaster preparedness. This study proposes an improved SD-K-Means clustering algorithm for classifying TC tracks. Using the best-track datasets of TCs from 2000 to 2022, provided by NOAA (National Oceanic and Atmospheric Administration) and JMA (Japan Meteorological Agency), it explores the quantitative relationships between various TC features, such as latitude, longitude, and wind speed, and their motion speed and deflection angles. Based on these analyses, clustering indicators coupled with TC tracks and motion characteristics are identified. To evaluate the model’s performance, three clustering methods—standard K-Means, DTW (Dynamic Time Warping)-based K-Means, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)—are compared using the Calinski–Harabasz (CH) index and the Davies–Bouldin Index (DBI) as evaluation metrics. The experimental results show that the SD-K-Means algorithm achieved high consistency across the majority of clustering indices, with the optimal number of clusters determined to be four. The spatial distribution of the clustering results demonstrates that SD-K-Means is effective in distinguishing different TC track patterns, providing valuable insights for regional disaster prevention and risk management efforts. Full article

Friction hysteresis, ingaphenomenon observed when a sliding contact is subjected to an oscillatory motion has significant implications in fields such as tribology and robotics. Understanding and quantifying friction hysteresis is essential for improving the performance and efficiency of many sliding contacts. In this paper, we introduce six non-dimensional groups to characterize and study friction hysteresis behaviour for rough conformal sliding contacts. The proposed non-dimensional groups are specifically designed to capture the essential features of friction hysteresis loops encountered based upon previous work of present authors. The non-dimensional groups are derived from a mixed friction model composed of the transient Reynolds equation, a statistical mixed friction contact model, and the load balance. The non-dimensional groups capture physical parameters that influence friction behaviour, including normal load, sliding speed, viscosity, density, and surface roughness. By expressing these parameters in non-dimensional form, the proposed groups provide a concise and generalizable framework for analysing friction hysteresis across different systems and scales. To demonstrate the effectiveness of the non-dimensional groups, we establish a comprehensive relationship between the proposed groups and typical friction hysteresis loops encountered. Through numerical simulations, we find relationships that govern the transition between different hysteresis loop shapes and sizes. This knowledge can inform the design and optimization of systems where friction hysteresis plays a crucial role. Full article

This study collected site and spatial morphological data from 63 typical aging community activity spaces and extracted 12 spatial types through statistical analysis. A parametric modeling tool was used to generate spatial models. Based on clearly defined design variables and constraints, the NSGA-II multi-objective optimization algorithm was applied to minimize summer thermal discomfort, maximize winter thermal comfort, and maximize annual average sunlight duration, resulting in 342 Pareto optimal solutions. The study first explored the linear relationships between spatial morphology and environmental performance using the Spearman method. It then integrated ensemble learning and the interpretable machine learning model SHAP to reveal nonlinear relationships and boundary effects. The results of the two methods complemented and reinforced each other. Based on a comparison of these two approaches, morphological indicators showing significant differences were selected for attribution and sensitivity analyses, clarifying the mechanisms by which spatial morphological parameters influence environmental performance and identifying their critical thresholds. Key findings include the following: (1) the UTCI-S exhibits significant negative linear correlations with the open space ratio (OSR) and spatial crowding density (SCD); the UTCI-W shows negative linear correlations with canopy coverage (CVH) and wind speed (WS); and a positive linear correlation exists between the sky view factor (SVF) and AV.SH. (2) Boundary effects and threshold intervals of critical morphological parameters were identified as follows. The open space ratio should be controlled to 10–15%, the shrub–tree layer coverage to 0.013–0.0165%, and the average building height to 3.1–3.8 m. (3) Spatial layout principles demonstrate that placing fully enclosed spaces (E-2) and semi-enclosed spaces (S-1/S-3) on the northern side, as well as semi-enclosed spaces (S-1/S-2) and circulation spaces (C-3) on the southern side, significantly enhance microclimatic performance. These findings provide quantitative guidelines for community space design in cold regions and offer data support for creating outdoor environments that meet the comfort needs of the elderly. Full article

To solve the problem of the boundary condition of the electrochemical field for a cathodic protection system of a steel offshore platform jacket, a new concept for the real electrochemical boundary condition was first proposed. The new idea considers that different points on the steel surface have different surface states and different polarization processes. The new method involved using sixteen sets of measurement equipment and a small test jacket to obtain different polarization processes at different points. A new test device was designed to obtain the relationship curves of potential/current density at different points. The polarization processes at different points were obtained. We first found that all polarization processes had four stages: rapid polarization, data jumping, polarization with middle speed, and slow polarization. At the end of the measurement, the current density interval exhibited a convergence phenomenon. The fitting curve based on the endpoint of the fourth stage of each relationship curve was regarded as the real boundary condition. The boundary condition was verified by the small test jacket and the real jacket. The comparison between the calculation and the measurement proved that the boundary condition was correct. The real boundary condition based on the new method reflected the real state and polarization process of the jacket and provided the correct incoming data for electrochemical field. Full article

To comparatively evaluate the suitability of superhydrophobic and superhydrophilic coatings for photovoltaic (PV) module surfaces in arid and low-rainfall regions, this study investigates their dust deposition mitigation performance under anhydrous conditions and assesses the impact of dust reduction on PV power generation efficiency. An experimental platform for dust deposition and a PV output measurement system were constructed to evaluate the performance of coated PV modules. The open-circuit voltage (U_oc), short-circuit current (I_sc), maximum power (P_max), and dust deposition mass were measured before and after dust exposure. Additionally, the influence of coating properties on dust deposition behavior and the correlation between dust deposition density and PV output power were systematically examined. The experimental data reveal a linear relationship between PV output power loss and dust deposition density. Dust accumulation decreases monotonically with panel tilt angle, while displaying a non-monotonic response to wind speed, peaking at 3.9 m/s. Under optimal conditions (60° tilt angle and 5.2 m/s wind speed), minimal dust deposition densities were observed: 0.25 g/m² for superhydrophobic coated PV modules versus 1.11 g/m² for superhydrophilic coated surfaces. Both superhydrophobic and superhydrophilic coatings demonstrated effective dust deposition inhibition in anhydrous environments. However, the dust deposition mitigation efficiency of the superhydrophobic coating (88.7%) is significantly better than that of the superhydrophilic coating (46.2%) under the working conditions of a large inclination angle (60°) and high wind speed (5.2 m/s). These findings provide critical experimental evidence for optimizing self-cleaning coating selection in PV modules deployed in arid regions. Full article

This study investigated the feasibility of fabricating self-lubrication material using fused deposition modelling (FDM) technology, focusing on the influence of printing parameters on tribological performance. Experiments were conducted using PA and ABS materials, with varying printing speed, infill density, and layer height across four levels. The research established regression equations and fitted curves to describe the relationship between printing parameters and the coefficient of friction (CoF). Validation experiments demonstrated the reliability of the models, with errors within 10%. The results indicate that reducing printing speed and increasing infill density enhance surface quality, with infill density exerting a more significant effect. The influence of layer height on surface quality depends on the printer characteristics, making precise quantification challenging. Additionally, this study confirms that resin-based samples produced via FDM exhibit self-lubricating potential. These findings contribute to the optimization of FDM-printed structures by balancing surface quality and tribological performance. Full article

Forest ecosystems are crucial in mitigating air pollution and improving air quality. Therefore, investigating the relationships between air quality, forest structure, and environmental factors in different forest types is of significant importance. This study conducted three months of continuous monitoring (June–September 2023) of air quality factors (particulate matter (PM_2.5 and PM₁₀), ozone (O₃), and negative air ions (NAI)) and environmental factors (air temperature (TA), relative humidity (RH), light intensity (LI), and wind speed (WS)) in four subtropical forest types, along with vegetation characteristic surveys. The effects of forest structure and environmental factors on air quality were determined by correlation and multiple regression analysis. The results showed that the forest air quality is at its best in July during the summer season. Concentrations of particulate matter (PM) and ozone (O₃) in mixed coniferous and broadleaf forests (MCB), as well as deciduous broadleaf forests (DB), are lower than those in moso bamboo forests (MB) and evergreen broadleaf forests (EB). The troughs of PM concentrations occur in the early morning (4:00–6:00), while the troughs of O₃ concentrations occur in the early morning (4:00–6:00) and in the evening (18:00). NAI concentrations were highest in DB (1287 ions/cm³), followed by MCB (1187 ions/cm³), MB (896 ions/cm³), and EB (584 ions/cm³), with NAI concentrations peaking between 14:00 and 16:00. PM concentrations in forest air were primarily influenced by stand density (SD) and the Shannon–Wiener index of herbaceous layer (SWH) (p < 0.05); ozone concentrations were significantly affected by tree height (TH) and canopy density (CD) (p < 0.05); and NAI concentrations were primarily related to TH and diameter at breast height (DBH). Air particulate matter concentrations were negatively affected by TA and RH (p < 0.01), and ozone concentrations were negatively influenced by RH and WS and were positively influenced by TA. TA has a direct and significant positive effect on the NAI concentration (p < 0.01), and RH indirectly influences the changes in NAI concentration through its interaction with TA. This study provides new insights for vegetation optimization in forest parks and planning forest health-promoting activities for sub-healthy populations. Full article