The effective allocation of emergency supplies is crucial in the aftermath of flood disasters, as it directly impacts response times and mitigates casualties and property losses. Traditional methods of material distribution predominantly rely on ground-based transportation, which often proves inefficient and inflexible under the dynamic conditions of a disaster. This study explores the potential of unmanned aerial vehicles (UAVs) as a transformative solution to the challenges associated with emergency material dispatch. Factors influencing UAV scheduling, including environmental constraints, payload capacity, and flight dynamics, are analyzed in depth. Optimization measures for improving UAV collaborative operations are proposed, with a focus on enhancing the efficiency and adaptability of disaster response systems. The integration of reinforcement learning (RL) is examined as a theoretical framework for optimizing UAV collaborative scheduling, facilitating autonomous decision-making in real-time scenarios. An empirical analysis is presented based on the “7-20” rainstorm and flooding disaster in Zhengzhou, illustrating the practical application of collaborative UAVs in disaster relief. The results demonstrate the significant optimization potential of UAV technology, with a notable reduction in response times and improved logistical coordination. Furthermore, the role of UAVs in future disaster relief operations is discussed, with emphasis on the integration of blockchain and smart dispatch systems to enable decentralized, autonomous coordination. These advancements are expected to enhance the overall efficiency of emergency material distribution and better address the complex challenges posed by post-disaster environments. The findings underscore the potential for UAV systems to revolutionize disaster management and contribute to more resilient, responsive strategies in future flood events.
In the ship hull plate welding process, different welding sequences directly affect the deformation of the current welding procedure, which in turn impacts the overall shipbuilding accuracy. This study takes a typical double T-shaped thin plate structure as an example. Based on welding numerical simulation and experimental validation, a corresponding dataset is obtained. To address the issue of BP neural networks being prone to local optima, which can lead to inaccurate results, a Simulated Annealing-Back Propagation (SA-BP) neural network model is used to analyze the dataset. The research aims to determine the optimal welding sequence that minimizes deformation. The training results show that the Mean Squared Error (MSE) of the SA-BP model decreased from 1.0144 in the BP model to 0.67388. Additionally, the SA-BP model's fitting performance is far superior to that of the BP model. Therefore, the SA-BP neural network model provides more stable and accurate results compared to the traditional BP neural network model. The comparison of the optimal welding sequence results derived from both models shows that welding with the optimized SA-BP neural network results in a 21.07% reduction in welding deformation compared to the traditional BP neural network.
In urban environments, the scarcity of available land often necessitates the construction of closely spaced, high-rise buildings, which rely heavily on pile foundations to support substantial loads. However, the pile-driving process, essential for such foundations, generates vibrations that can propagate through the ground and affect surrounding structures, potentially leading to adverse consequences. These vibrations can disrupt the comfort of residents and cause structural damage to adjacent buildings, including residential properties, hotels, and hospitals, where both the comfort and safety of occupants are of paramount importance. Furthermore, pile-driving-induced vibrations can result in the development of cracks in the architecture, settlement of foundations, or even severe structural failure in sensitive installations. To assess the effects of pile-driving on nearby buildings, a series of 77 finite element models were developed using PLAXIS 3D, which simulated varying pile-to-building distances and driving depths. The analyses focused on both the comfort of residents and the structural integrity of adjacent buildings, with comparisons drawn against international standards for vibration levels. The results revealed that the optimal driving depth could effectively minimize peak vibration levels, thereby reducing the risk of disruption to nearby structures. Additionally, the influence of parameters such as pile-driving load, pile penetration depth, and soil characteristics on vibration propagation was systematically explored. The findings provide critical insights into the mitigation of pile-driving-induced vibrations in urban settings and offer guidance for optimizing pile-driving operations to safeguard both resident comfort and structural safety.
Cutoff walls are an essential method for seepage prevention in dams. During the construction and operation of reservoirs, factors such as construction techniques, variations in groundwater conditions within the dam body, geological movements, and climatic factors may lead to potential seepage risks, necessitating inspection. Traditional methods like borehole coring and water pressure tests have limited monitoring ranges, while non-destructive methods like high-density electrical surveys and shallow seismic exploration have low deep-resolution capabilities, making them unsuitable for detecting deep-seated seepage in concrete walls. In recent years, Cross-borehole Tomography (CT) geophysical techniques, based on boreholes on both sides, have been widely applied in various engineering geophysical projects. Seepage in cutoff walls can lead to an increase in local moisture content, resulting in low-resistivity anomalies, providing a physical basis for the exploration using cross-borehole resistivity CT. This study investigates the resistivity response characteristics of cross-borehole resistivity CT through numerical simulation based on the resistivity characteristics of seepage in cutoff walls. The numerical simulation results indicate that this method effectively identifies seepage conditions in cutoff walls, and the resolution of cross-borehole resistivity CT is significantly related to the cross-hole spacing and the distance to the seepage points. This study provides a preliminary verification of the feasibility of applying cross-borehole resistivity CT for detecting seepage in cutoff walls and offers insights for seepage detection strategies.
Crowdsourced delivery, a pivotal component of crowd logistics, represents a transformative model for optimizing logistics resources through the efficient allocation of available capacities, thus responding to the flexibility demands of contemporary businesses. At the heart of this model are digital platforms that facilitate the coordination of activities between couriers, users, and service providers. In Serbia, several prominent platforms stand out due to their advanced functionalities, extensive product offerings, and rapid delivery capabilities. Simultaneously, smaller platforms face significant challenges in maintaining competitiveness within an increasingly saturated market. Despite the numerous advantages offered by the crowdsourcing model, couriers engaged in this sector encounter a variety of obstacles that undermine its full potential. These challenges encompass issues related to working conditions, contractual arrangements, and the stability and security of courier incomes, all of which are essential to the sustainability of the system. A survey was conducted to gain an in-depth understanding of the couriers' perspectives on the operational dynamics of crowdsourced delivery. The study aimed to gather empirical data on the daily challenges faced by couriers, their working conditions, job satisfaction, and relationships with platform companies. Additionally, insights were sought into the overall functioning of crowd logistics systems from the perspective of the couriers, with a particular focus on identifying areas where improvements could be made to enhance the working conditions and status of couriers. The findings are expected to inform strategies that could mitigate the current challenges, thereby contributing to a more equitable and efficient model of crowdsourced delivery. This research highlights the importance of addressing the couriers' concerns as a critical step toward the optimization of crowdsourcing logistics systems and the enhancement of their long-term viability.
Foggy road conditions present substantial challenges to road monitoring and autonomous driving systems, as existing defogging techniques often fail to accurately recover structural details, manage dense fog, and mitigate artifacts. In response, a novel defogging model is proposed, incorporating Pythagorean fuzzy aggregation, Gaussian Mixture Models (GMM), and the level-set method, aimed at overcoming these limitations. Unlike conventional methods that depend on fixed priors or oversimplified haze models, the proposed framework leverages the advantages of Pythagorean fuzzy aggregation to enhance contrast and detail restoration, GMM to estimate fog density robustly, and the level-set method for precise edge preservation. The performance of the model is quantitatively assessed, revealing a Peak Signal-to-Noise Ratio (PSNR) of up to 37.1 dB and a Structural Similarity Index (SSIM) of 0.96, which significantly outperforms existing defogging techniques. Statistical analyses further confirm the robustness of the approach, with a p-value of less than 0.001 for key performance metrics. Additionally, the model demonstrates an execution time of 0.07 seconds, indicating its suitability for real-time road monitoring applications. Qualitative assessments highlight the model's ability to restore natural road colours and maintain high structural fidelity, even under conditions of dense fog. This work provides a promising advancement over current methods, with potential applications in autonomous driving, traffic surveillance, and smart transportation systems.
In recent years, representing computer vision data in tensor form has become an important method of data representation. However, due to the limitations of signal acquisition devices, the actual data obtained may be damaged, such as image loss, noise interference, or a combination of both. Using Low-Rank Tensor Completion (LRTC) techniques to recover missing or corrupted tensor data has become a hot research topic. In this paper, we adopt a tensor coupled total variation (t-CTV) norm based on t-SVD as the minimization criterion to capture the combined effects of low-rank and local piecewise smooth priors, thus eliminating the need for balance parameters in the process. At the same time, we utilize the Non-Local Means (NLM) denoiser to smooth the image and reduce noise by leveraging the nonlocal self-similarity of the image. Furthermore, an Alternating Direction Method of Multipliers (ADMM) algorithm is designed for the proposed optimization model, NLM-TCTV. Extensive numerical experiments on real tensor data (including color, medical, and satellite remote sensing images) show that the proposed method has good robustness, performs well in noisy images, and surpasses many existing methods in both quality and visual effects.
A novel composite mechanical bulging process suitable for the manufacture of medium-duty commercial vehicle drive axle housings is proposed. The analytical expression for the limit bulging forming coefficient of tube blanks under conditions below the metal recrystallization temperature is derived, and the influence of the matching of various force parameters on the limit bulging forming coefficient is analyzed. The appropriate range for the axial auxiliary load during radial bulging is also presented. Based on the derived theory, a 5-ton commercial vehicle drive axle housing is selected as the research object. The key processes in the forming process are numerically simulated to obtain the metal flow state, stress-strain distribution, and wall thickness variation. The types and locations of defects that may occur during the bulging process are also predicted. To address the phenomenon of local wall thinning in the composite mechanical bulging process of the drive axle housing, a set of orthogonal simulation experiments is designed, focusing on the wall thickness thinning rate in the bridge arch bulging area and the crack-prone region, with respect to the process parameters. Based on the numerical simulation results, response surface equations are established for the expansion core's movement speed and axial auxiliary thrust in relation to the wall thickness thinning rate. Through parameter estimation of the response surface equation and regression analysis of significant influencing factors, the effects of process parameters on wall thickness thinning are obtained: the thinning rate in the bridge arch bulging area decreases with increasing expansion core movement speed and axial auxiliary thrust, while the thinning rate in the crack-prone region increases. The optimization of the response surface model and the determination of the optimal process parameter combination, based on field production conditions, show that the numerical simulation results and the wall thickness measurements from process experiments are in close agreement. No cracks occur in the axle housing, and the thinning is effectively alleviated. In contrast, mechanical bulging without axial auxiliary thrust leads to cracks, thus validating the feasibility of the proposed process scheme and the effectiveness of the parameter optimization. This research provides valuable technical reference for upgrading the manufacturing technology of large-span axle-tube products.
In the context of rapidly advancing digital technology, where touchscreen interactions dominate, the tactile sensory development of children is increasingly compromised. This shift towards digital media can hinder the ability of children to effectively engage with and observe their surroundings. One promising solution to this issue is the integration of art appreciation into educational practices. However, in regions such as Indonesia, there is a noticeable scarcity of comprehensive learning kits aimed at teaching art appreciation. This study addresses this gap by designing and developing an art appreciation learning kit intended for children aged 7 to 11, aiming to teach art appreciation through artful thinking (AT). The kit employs the “see, think, wonder” (STW) thinking routine, a structured three-step process that encourages children to observe art, analyze their observations, and engage with the art through inquiry. The analysis, design, development, implementation, and evaluation (ADDIE) framework was employed as the instructional design model. Additionally, a qualitative research through design (RtD) methodology was adopted to guide the design process and ensure the creation of an innovative educational tool. The developed learning kit integrates physical components, multimedia resources, and hands-on arts and crafts activities that complement the STW routine, thereby fostering deeper engagement and critical thinking skills among young learners. The study emphasizes the value of employing the ADDIE model to assess the learning needs and challenges faced by children, particularly during the STW activity. Collaboration with educators during the design and development phases was identified as crucial for refining the learning kit. Key recommendations include the integration of graphic visualizations, clear demonstrations, and interactive activities to enhance children’s engagement and enthusiasm for art appreciation. The findings offer empirical evidence supporting the effective use of the ADDIE model in educational kit design, providing a valuable reference for future product designers in the educational technology field.
Individuals with disabilities have long faced disproportionate economic disadvantages, including higher poverty rates, poorer health outcomes, limited access to education, and restricted employment opportunities compared to those without disabilities. The green economy, characterized by low carbon emissions, resource efficiency, and social inclusivity, holds the potential to address these persistent inequities by creating jobs that promote income equality and support sustainable livelihoods. However, despite the growing global shift toward carbon neutrality, there is a significant gap in understanding the challenges and opportunities faced by persons with disabilities in this transition. This scoping review aims to assess the current state of knowledge regarding the inclusion of persons with disabilities in the green economy, with a particular focus on the Global North. Literature published between 2012 and 2023 was systematically reviewed, resulting in the identification of 21 relevant studies from an initial pool of 4,311 abstracts. The findings were categorised into three primary themes: conceptual frameworks for inclusion in the green economy, the role of persons with disabilities as workers, and the role of persons with disabilities as consumers. The results underscore a critical lack of literature addressing disability inclusion in green economic development, with existing studies indicating that persons with disabilities have been systemically marginalized in efforts to foster low-carbon economies. This exclusion represents a missed opportunity to harness the talents, perspectives, and contributions of persons with disabilities, whether as workers, consumers, or agents of change in sustainable development. It is therefore imperative that the experiences and epistemologies of persons with disabilities are central to the design, planning, and implementation of green economy initiatives. Future research must address the existing gaps in the literature and explore strategies for fostering greater inclusion in green economic frameworks to ensure equitable opportunities for all individuals in the transition to a carbon-neutral world.
Nanofluids, which are suspensions of nanoparticles in base fluids, have demonstrated considerable potential in enhancing thermal conductivity, energy storage, and lubrication properties, as well as improving the cooling efficiency of electronic devices. Despite their promising applications, the industrial utilization of nanofluids remains in the early stages, with further research needed to fully explore their capabilities. This study investigates a generalized nanofluid model, incorporating fractal-fractional derivative (FFD), to better understand the thermophysical behaviors in vertical channel flow. The nanofluid consists of polystyrene nanoparticles uniformly dispersed in kerosene oil. An exact solution to the model is obtained by employing the Laplace transform technique (LTT) in combination with the numerical Zakian’s algorithm. The FFD operator with an exponential kernel is applied to extend the classical nanofluid model. Discretization of the generalized model is achieved using the Crank-Nicolson method, and numerical simulations are performed to solve the resulting equations. The study reveals that, at a nanoparticle volume fraction of 4% (0.04), the heat transfer rate of the nanofluid is significantly higher than that of the base fluid. Furthermore, the enhanced heat transfer leads to improvements in various thermophysical properties, such as viscosity, thermal expansion, and heat capacity, which are crucial for industrial applications. The numerical results are presented graphically to highlight the dependence of the flow and thermal dispersion characteristics on key physical factors. These findings suggest that the use of fractal-fractional models can provide a more accurate representation of nanofluid behavior, particularly for high-precision applications in heat transfer and energy systems.
This study investigates the application of the horizontal stratified mining method to the extraction of steeply inclined unstable coal seams at the Puxi Mine. The stress environment in the mining area, the relationship between the supports and surrounding rock, the control of the rock layers in the caving zone, and the mechanical analysis of the roof collapse following the extraction of the steeply inclined coal seam were examined. The stress conditions in the mining area under the horizontal stratified mining method were explored, and a numerical analysis model was established using FLAC3D software, based on the rock mechanics parameters of the Puxi Mine’s rock layers and strata. The results indicate that, in the stress environment of the horizontal stratified mining method, the mining area is subject to not only the self-weight stress from the surrounding strata, large horizontal ground stresses, and gas pressures, but also concentrated stresses in both the dip and strike directions. When using this mining method, the stability of the two sides of the tunnel is generally good due to the surrounding rock being of a relatively stable nature. However, the roof collapse in the upper layers during the extraction of the lower layers is one of the factors affecting the safety of the support structures in the lower layers, necessitating enhanced support management. Deformation is expected in the mining face of the lower layers during extraction, and measures must be taken to prevent any instances of roof spalling. Therefore, the horizontal stratified mining method is considered feasible for the extraction of steeply inclined unstable coal seams at the Puxi Mine.
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