Search Results (7,222)

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace. Full article

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability. Full article

The atomization mechanism of lubrication fluid in rolling bearings under high-speed airflow between the rings was investigated. A simulation model of gas–liquid two-phase flow in angular contact ball bearings was developed, and the jet lubrication process between the bearing rings was simulated using FLUENT computational fluid dynamics software (Ansys 19.2). The complex motion boundary conditions of the rolling elements were addressed through a layered approach. We can obtain more accurate boundary layer flow field changes and statistics of the diameter of oil particles in lubricating oil atomization, which lays the foundation for analyzing the law of influence on lubricating oil atomization. The results show that as the number of boundary layer layers increases, the influence of the boundary layer flow field on the lubricating oil is more obvious. The oil particle size is excessively flat, and the concentration of large particles of oil appears to decrease. As the speed increases, the amount of oil in the cavity decreases, but the oil droplets are also fragmented, which intensifies the atomization and reduces the particle diameter. This reduces the Sauter Mean Diameter (SMD), which is not conducive to the lubrication of the bearing. Under different injection pressures, when the injection pressure is large, it is beneficial to the lubrication of the bearing. Full article

To address the challenges of complex backgrounds and small, easily confused fire and smoke targets in Unmanned Aerial Vehicle (UAV)-based forest fire detection, we propose an improved forest smoke and fire detection algorithm based on YOLOv8. Considering the limited computational resources of UAVs and the lightweight property of YOLOv8n, the original model of YOLOv8n is improved, the Bottleneck module is reconstructed using Group Shuffle Convolution (GSConv), and the residual structure is improved, thereby enhancing the model’s detection capability while reducing network parameters. The GBFPN module is proposed to optimize the neck layer network structure and fusion method, enabling the more effective extraction and fusion of pyrotechnic features. Recognizing the difficulty in capturing the prominent characteristics of fire and smoke in a complex, tree-heavy environment, we implemented the BiFormer attention mechanism to boost the model’s ability to acquire multi-scale properties while retaining fine-grained features. Additionally, the Inner-MPDIoU loss function is implemented to replace the original CIoU loss function, thereby improving the model’s capacity for detecting small targets. The experimental results of the customized G-Fire dataset reveal that FGYOLO achieves a 3.3% improvement in mean Average Precision (mAP), reaching 98.8%, while reducing the number of parameters by 26.4% compared to the original YOLOv8n. Full article

This work presents a facile way to fabricate a polymer/ceramics composite gel electrolyte to improve the overall properties of lithium-ion batteries. Lithium salt-grafted silica was synthesized and mixed with P(VDF-HFP) to produce a nanofiber film by the electrostatic spinning method. After coating a layer of SiO₂ onto the surface of nanofibers through a sol-gel method, a composite nanofiber film was obtained. It was then immersed in plasticizer until saturation to make a composite gel electrolyte film. Electrochemical test results showed that the obtained gel electrolyte film shows high thermal stability (~450 °C), high ionic conductivity of 1.3 × 10⁻³ S cm⁻¹ at 25 °C and a lithium-ion transference number of 0.58, and superior cycling stability, providing a new direction for manufacturing secondary batteries with higher safety and performance. Full article

The episodes of elevated concentrations of different gaseous pollutants and particulate matter (PM) are of major concern worldwide, especially in city agglomerations. Krakow is an example of an urban–industrial agglomeration with constantly occurring PM₁₀ air limit value exceedances. In recent years, a number of legislative actions have been undertaken to improve air quality in this area. The multitude of factors affecting the emergence of cases of very high air pollutant concentrations makes it difficult to analyze them using simple statistical methods. Machine learning (ML) methods can be an adequate option, especially when proper amounts of credible data are available. The main aim of this paper was to examine the influence of various factors (including main gaseous pollutant concentrations and some meteorological factors) on the effect of high PM₁₀ concentration episodes in the ambient air in Krakow (Poland) using the random forest algorithm. The original methodology based on the PM₁₀ limit and binary classification of cases with and without the occurrence of high concentration episodes was developed. The data used were derived from routine public air quality monitoring and a local meteorological station. A range of random forest classification models with various predictor sets and for different subsets of the observations coupled with variable importance analysis were performed. The performance of the algorithm was assessed using confusion matrices. The variable importance rankings revealed, among other things, the dominant impact of the mixing layer height on elevated PM₁₀ concentration episode formation. This research work showed the usefulness of the random forest algorithm in identifying factors contributing to poor air quality, even in the absence of reliable emission data. Full article

The high sodium content in meat products poses health risks to consumers and does not align with modern green and healthy living standards. Current strategies for directly reducing the sodium content in meat products are limited by their negative impact on the sensory or quality attributes of the products. In recent years, there has been great interest in applying ultrasound technology to reduce sodium content. This paper discusses the advantages and disadvantages of current mainstream strategies for reducing the sodium content in meat products, as well as the potential mechanisms by which ultrasound-assisted marination improves the quality of low-salt meat products. The main findings indicate that ultrasound, through its cavitation and mechanical effects, facilitates the transition of proteins from stable insoluble aggregates to stable soluble complexes, exposing more hydrophilic groups and, thus, enhancing protein solubility. At the same time, ultrasound promotes a greater number of proteins to participate in the formation of interfacial layers, thereby increasing emulsifying activity. Furthermore, ultrasound treatment promotes the interaction between proteins and water, leading to partial unfolding of protein chains, which allows polar residues to more readily capture water in the gel, thereby improving the water-holding capacity of the gel. These effects will contribute to the formation of high-quality low-salt meat products. However, variations in the frequency, intensity, and duration of ultrasound treatment can lead to differing effects on the quality improvement of low-salt meat products. Full article

The straight part of the corrugated steel plate (CSP) pipe-arch structure in soil may cause local buckling instability due to insufficient load-bearing capacity. Recently, composite CSP pipe-arch has been widely utilized to enhance structural stability, and their stability needs to be thoroughly investigated. This paper studies the local buckling stability problem of the straight part of composite CSP pipe-arch in soil by simplifying the soil support and introducing the inter-layer bonding effect. Based on elastic stability theory, a theoretical mechanical model of composite CSP pipe-arch was proposed. The Rayleigh–Ritz method and the semi-combined composite structure stiffness approximation were used to derive the critical buckling conditions for the straight part of the composite CSP pipe-arch. Through numerical calculation and influencing factors analysis, it is concluded that the critical buckling load of the straight part of the composite CSP pipe-arch structure is affected by the elastic modulus, thickness, Poisson’s ratio, rotational restraint stiffness and side length of the straight part of the material. In particular, it is found that as the inter-layer bonding coefficient increases, the critical buckling load is improved, while the critical buckling wave number is mainly influenced by the width of the straight part, elastic modulus, and inter-layer bonding coefficient. Additionally, we discussed the coupling effect of several key parameters on the stability of the structure. The results of this study offer theoretical foundations and guidance for the application of composite CSP pipe-arch in soil engineering, such as culverts, tunnels, and pipeline transportation. Full article

With the advent of the 6G era, the number of IoT devices has experienced explosive growth, leading to the generation of massive amounts of data at the network edge. Semantic communication, as an innovative solution to handling this data deluge, can significantly enhance communication efficiency. However, the limited storage and computational resources of terminal devices constrain the widespread application of semantic communication in 6G networks. To address this issue, we propose a terminal-side-computing-driven intelligent semantic communication solution. Specifically, we introduce a semantic communication model based on Kolmogorov–Arnold Networks (KANs), named K-DeepSC. Using image-reconstruction tasks as an example, the proposed K-DeepSC reduces the number of model parameters by 44% compared to semantic communication models based on Multi-Layer Perceptrons (MLPs), while maintaining similar performance. Furthermore, to fully leverage idle terminal computing power for semantic tasks, we explore computation offloading in dynamic Terminal-Side Computing Power Networks. By optimizing task delay minimization, a deep reinforcement learning algorithm is employed to determine the optimal offloading strategy. Simulation results demonstrate that our proposed solution effectively reduces semantic task processing delay. Full article

In this study, the effectiveness of Fe₃O₄-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe₃O₄-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe₃O₄-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe₃O₄-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R²) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe₃O₄-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings. Full article

The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively. Full article

This study is dedicated to the investigation of the relationship between the wave activity in February and temperature variations in the Arctic lower stratosphere in March. To study this relationship, the correlation coefficients (CCs) between the minimum temperature of the Arctic lower stratosphere in March (Tmin) and the amplitude of the planetary wave with zonal number 1 (PW1) in February were calculated. Tmin determines the conditions for the formation of polar stratospheric clouds (PSCs) following the chemical destruction of the ozone layer. The NCEP and ERA5 reanalysis data and the modern and future climate simulations of the Earth system models INM CM5 and SOCOLv4 were employed. It is shown that the maximum significant CC value between Tmin at 70 hPa in the polar region in March and the amplitude of the PW1 in February in the reanalysis data in the lower stratosphere is 0.67 at the pressure level of 200 hPa. The CCs calculated using the model data are characterized by maximum values of ~0.5, also near the same pressure level. Thus, it is demonstrated that the change in the planetary wave activity in the lower extratropical stratosphere in February can be one of the predictors of the Tmin. For further analysis of the dynamic structure in the lower stratosphere, composites of 10 seasons with the lowest and highest Tmin of the Arctic lower stratosphere in March were assembled. For these composites, differences in the vertical distribution and total ozone content, surface temperature, and residual meridional circulation (RMC) were considered, and features of the spatial distribution of wave activity fluxes were investigated. The obtained results may be useful for the development of forecasting of the Arctic winter stratosphere circulation, especially for the late winter season, when substantial ozone depletion occurs in some years. Full article

Currently, NiFe-LDH exhibits an excellent oxygen evolution reaction (OER) due to the interaction of the two metal elements on the layered double hydroxide (LDH) platform. However, such interaction is still insufficient to compensate for its poor electrical conductivity, limited number of active sites and sluggish dynamics. Herein, a feasible two-step hydrothermal strategy that involves coupling low-conductivity NiFe-LDH with 3D porous graphene aerogel (GA) is proposed. The optimized NiFe-LDH/GA (1:1) produced possesses a 257 mV (10 mA cm⁻²) overpotential and could operate stably for 56 h in an OER. Our investigation demonstrates that the NiFe-LDH/GA has a three-dimensional mesoporous structure, and that there is synergistic interaction between LDH and GA and interfacial reconstruction of NiOOH. Such an interface synergistic coupling effect promotes fast mass transfer and facilitates OER kinetics, and this work offers new insights into designing efficient and stable GA-based electrocatalysts. Full article

This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%. Full article

Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment. Full article