Search Results (199)

Hierarchically porous polymers can unite macro-scale architected voids with micro-scale pores, enabling unique combinations of low density, high surface area, and controlled transport properties that are difficult to achieve with traditional methods. This review outlines the current advancements in creating such multiscale architectures using fused filament fabrication (FFF), the most widely used polymer additive manufacturing technique. Unlike earlier reviews that consider lattice architectures and foaming chemistries separately, this work integrates both within a single analysis. It begins with an overview of FFF fundamentals and how process parameters affect macropore formation. Design strategies for achieving macroporosity (≳100 µm) with a single thermoplastic are presented and categorized: 2D infill patterns, strut-based lattices, triply periodic minimal surfaces (TPMS), and Voronoi structures, along with functionally graded approaches. The discussion then shifts to functional filaments incorporating chemical or physical blowing agents, thermally expandable or hollow microspheres, and sacrificial porogens, which create microporosity (≲100 µm) either in situ or through post-processing. Each material approach is connected to case studies that demonstrate its application. A comparative analysis highlights the advantages of each method. Key challenges such as viscosity control, thermal gradient management, dimensional instability during foaming, environmental concerns, and the absence of standardized porosity measurement techniques are addressed. Finally, emerging solutions and future directions are explored. Overall, this review provides a comprehensive perspective on strategies that enhance FFF’s capability to fabricate hierarchically porous polymer structures. Full article

Classification prediction technology, which utilizes labeled data for training to enable autonomous decision, has emerged as a pivotal tool across numerous fields. The Elman neural network (ENN) exhibits potential in tackling nonlinear problems. However, its computational process faces inherent limitations in escaping local optimum and experiencing a slow convergence rate. To improve these shortcomings, an ENN classifier based on Hyperactivity Rat Swarm Optimizer (HRSO), named HRSO-ENNC, is proposed in this paper. Initially, HRSO is divided into two phases, search and mutation, by means of a nonlinear adaptive parameter. Subsequently, five search actions are introduced to enhance the global exploratory and local exploitative capabilities of HRSO. Furthermore, a stochastic roaming strategy is employed, which significantly improves the ability to jump out of local positions. Ultimately, the integration of HRSO and ENN enables the substitution of the original gradient descent method, thereby optimizing the neural connection weights and thresholds. The experiment results demonstrate that the accuracy and stability of HRSO-ENNC have been effectively verified through comparisons with other algorithm classifiers on benchmark functions, classification datasets and an AlSi10Mg process classification problem. Full article

Thermal effects critically influence the design and construction of steel-concrete composite cable-stayed bridges, where material thermal mismatch complicates structural responses. Current code-specified temperature gradient models inadequately address long-span bridges. This study employs in-situ monitoring of the Chibi Yangtze River Bridge to propose a refined vertical temperature gradient model, utilizing an exponential function for the concrete deck and a linear function for the steel web. Finite element analysis across six construction stages reveals: (1) Under negative temperature gradients, the concrete deck develops tensile stresses (2.439–2.591 MPa), approximately 30% lower than code-predicted values (3.613–3.715 MPa), highlighting risks of longitudinal cracking. (2) At the maximum double-cantilever stage, transverse stress distributions show pronounced shear lag effects, positive shear lag in deck sections connected to crossbeams and negative shear lag in non-connected sections. The proposed model reduces tensile stress conservatism in codes by 30–33%, enhancing prediction accuracy for composite girders. This work provides critical insights for thermal effect management in long-span bridge construction. Full article

Aiming at the problem of poor autonomy and weak time performance of reentry trajectory planning for Reusable Launch Vehicle (RLV), an online reentry trajectory planning and guidance method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. In view of the advantage that the drag acceleration can be quickly measured by the airborne inertial navigation equipment, the reference profile adopts the design of the drag acceleration–velocity profile in the reentry corridor. In order to prevent the problem of trajectory angle jump caused by the unsmooth turning point of the section, the section form adopts the form of four multiple functions to ensure the smooth connection of the turning point. Secondly, considering the advantages of the TD3 dual Critic network structure and delay update mechanism to suppress strategy overestimation, the TD3 algorithm framework is used to train multiple strategy networks offline and output profile parameters. Finally, considering the reentry uncertainty and the guidance error caused by the limitation of the bank angle reversal amplitude during lateral guidance, the networks are invoked online many times to solve the profile parameters in real time and update the profile periodically to ensure the rapidity and autonomy of the guidance command generation. The TD3 strategy networks are trained offline and invoked online many times so that the cumulative error in the previous guidance period can be eliminated when the algorithm is called again each time, and the online rapid generation and update of the reentry trajectory is realized, which effectively improves the accuracy and computational efficiency of the landing point. Full article

This study adopts numerical simulation methods to explore the acoustic transmission characteristics of pipe strings in the upper and lower completions of a monitoring system for test production of natural gas hydrate. A finite-element simulation model for acoustic transmission in the pipe string system is established through COMSOL. The sound pressure level attenuation and the sound pressure amplitude ratio are chosen as evaluation indexes. Parametric numerical simulations are carried out to study the effects of the number of tubing cascades and the size of connection joints in the pipe string system on the acoustic transmission characteristics of the pipe string. The Light Gradient Boosting Machine (LightGBM) algorithm is adopted to predict the acoustic transmission characteristic curves of the pipe string. Based on this prediction model, with the maximum transmission distance, maximum sound pressure amplitude ratio, and minimum transmission attenuation as objective functions, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) optimization algorithm is adopted to obtain the optimal combinations of the pipe string system structure and the transmission frequency. The findings show that within the range of 20–2000 Hz, when the acoustic wave propagates in the column system, the amplitude attenuation caused by structural damping is positively correlated with the transmission distance, and the high-frequency acoustic wave attenuates faster. When the frequency exceeds 500 Hz, the sound pressure amplitude ratio is lower than 0.4, and the attenuation is stabilized at 90% above 1500 Hz. The thickness of the joints has a weak impact on the transmission, while an increase in length raises the characteristic frequency but exacerbates sound pressure attenuation. The LightGBM algorithm has a high prediction accuracy, reaching up to 88.54% and 84.82%, respectively. The optimal parameter combinations (n, hkg, lkg, freq) optimized by NSGA-II provide an optimization scheme for the structure and frequency of acoustic transmission in down-hole pipe strings. Full article

Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition method based on image expansion and Dynamic Model-Agnostic Meta-Learning (DMAML) is proposed. In terms of data augmentation, a multi-connected conditional generative network is designed for generating palmprints; the network is trained using a gradient-penalized hybrid loss function and a dual time-scale update rule to help the model converge stably, and the trained network is used to generate an expanded dataset of palmprints. On this basis, the palmprint feature extraction network is designed considering the frequency domain and residual inspiration to extract the palmprint feature information. The DMAML training method of the network is investigated, which establishes a multistep loss list for query ensemble loss in the inner loop. It dynamically adjusts the learning rate of the outer loop by using a combination of gradient preheating and a cosine annealing strategy in the outer loop. The experimental results show that the palmprint dataset expansion method in this paper can effectively improve the training efficiency of the palmprint recognition model, evaluated on the Tongji dataset in an N-way K-shot setting, our proposed method achieves an accuracy of 94.62% ± 0.06% in the 5-way 1-shot task and 87.52% ± 0.29% in the 10-way 1-shot task, significantly outperforming ProtoNets (90.57% ± 0.65% and 81.15% ± 0.50%, respectively). Under the 5-way 1-shot condition, there was a 4.05% improvement, and under the 10-way 1-shot condition, there was a 6.37% improvement, demonstrating the effectiveness of our method. Full article

Existing methods for diagnosing depression rely heavily on subjective evaluations, whereas electroencephalography (EEG) emerges as a promising approach for objective diagnosis due to its non-invasiveness, low cost, and high temporal resolution. However, current EEG analysis methods are constrained by volume conduction effect and class imbalance, both of which adversely affect classification performance. To address these issues, this paper proposes a multi-stage deep learning model for EEG-based depression classification, integrating a cortical feature extraction strategy (CFE), a feature attention module (FA), a graph convolutional network (GCN), and a focal adversarial domain adaptation module (FADA). Specifically, the CFE strategy reconstructs brain cortical signals using the standardized low-resolution brain electromagnetic tomography (sLORETA) algorithm and extracts both linear and nonlinear features that capture cortical activity variations. The FA module enhances feature representation through a multi-head self-attention mechanism, effectively capturing spatiotemporal relationships across distinct brain regions. Subsequently, the GCN further extracts spatiotemporal EEG features by modeling functional connectivity between brain regions. The FADA module employs Focal Loss and Gradient Reversal Layer (GRL) mechanisms to suppress domain-specific information, alleviate class imbalance, and enhance intra-class sample aggregation. Experimental validation on the publicly available PRED+CT dataset demonstrates that the proposed model achieves a classification accuracy of 85.33%, outperforming current state-of-the-art methods by 2.16%. These results suggest that the proposed model holds strong potential for improving the accuracy and reliability of EEG-based depression classification. Full article

Efficient autonomous exploration in unknown obstacle cluttered environments with interior obstacles remains a challenging task for mobile robots. In this work, we present a novel exploration process for a non-holonomic agent exploring 2D spaces using onboard LiDAR sensing. The proposed method generates velocity commands based on the calculation of the solution of an elliptic Partial Differential Equation with Dirichlet boundary conditions. While solving Laplace’s equation yields collision-free motion towards the free space boundary, the agent may become trapped in regions distant from free frontiers, where the potential field becomes almost flat, and consequently the agent’s velocity nullifies as the gradient vanishes. To address this, we solve a Poisson equation, introducing a source point on the free explored boundary which is located at the closest point from the agent and attracts it towards unexplored regions. The source values are determined by an exponential function based on the shortest path of a Hybrid Visibility Graph, a graph that models the explored space and connects obstacle regions via minimum-length edges. The computational process we apply is based on the Walking on Sphere algorithm, a method that employs Brownian motion and Monte Carlo Integration and ensures efficient calculation. We validate the approach using a real-world platform; an AmigoBot equipped with a LiDAR sensor, controlled via a ROS-MATLAB interface. Experimental results demonstrate that the proposed method provides smooth and deadlock-free navigation in complex, cluttered environments, highlighting its potential for robust autonomous exploration in unknown indoor spaces. Full article

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of high-frequency details, high computational complexity, and insufficient adaptability to complex scenes. To address these challenges, we propose an Omni-dimensional Dynamic Convolutional Network (ODConvNet) tailored for SISR tasks. Specifically, ODConvNet comprises four key components: a Feature Extraction Block (FEB) that captures low-level spatial features; an Omni-dimensional Dynamic Convolution Block (DCB), which utilizes a multidimensional attention mechanism to dynamically reweight convolution kernels across spatial, channel, and kernel dimensions, thereby enhancing feature expressiveness and context modeling; a Deep Feature Extraction Block (DFEB) that stacks multiple convolutional layers with residual connections to progressively extract and fuse high-level features; and a Reconstruction Block (RB) that employs subpixel convolution to upscale features and refine the final HR output. This mechanism significantly enhances feature extraction and effectively captures rich contextual information. Additionally, we employ an improved residual network structure combined with a refined Charbonnier loss function to alleviate gradient vanishing and exploding to enhance the robustness of model training. Extensive experiments conducted on widely used benchmark datasets, including DIV2K, Set5, Set14, B100, and Urban100, demonstrate that, compared with existing deep learning-based SR methods, our ODConvNet method improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the visual quality of SR images is also improved. Ablation studies further validate the effectiveness and contribution of each component in our network. The proposed ODConvNet offers an effective, flexible, and efficient solution for the SISR task and provides promising directions for future research. Full article

Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km²) to 37 (725 km²), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective. Full article

The coastal zone presents complex hydrodynamic interactions among inland groundwater, reservoir water, and intruding seawater, with important implications for ecosystem functioning and water quality. However, the relative roles of hydraulic connectivity and seawater-driven salinity gradients in shaping microbial communities at the aquifer–reservoir interface remain unclear. Here, we integrated hydrochemical analyses with high-throughput 16S rRNA gene sequencing to investigate bacterial community composition, assembly processes, and co-occurrence network patterns across groundwater_in (entering the reservoir), groundwater_out (exiting the reservoir), and reservoir water in a coastal system. Our findings reveal that seawater intrusion exerts a stronger influence on groundwater_out, leading to distinct chemical profiles and salinity-driven environmental filtering, whereas hydraulic connectivity promotes greater microbial similarity between groundwater_in and reservoir water. Groundwater samples exhibited higher alpha and beta diversity compared to the reservoir, with dominant taxa such as Comamonadaceae, Flavobacteriaceae, and Rhodobacteraceae serving as indicators of seawater intrusion. Community assembly analyses showed that homogeneous selection predominated, especially under strong salinity gradients, while dispersal limitation and spatial distance also contributed in areas of reduced connectivity. Key chemical factors, including TDS, Na⁺, Cl⁻, Mg²⁺, and K⁺, strongly shaped groundwater communities. Additionally, groundwater bacterial networks were more complex and robust than those in reservoir water, suggesting enhanced resilience to salinity stress. Collectively, this study demonstrates that salinity gradients can override the effects of hydraulic connectivity in structuring bacterial communities and their networks at coastal interfaces. Our findings provide novel microbial insights relevant for understanding biogeochemical processes and support the use of microbial indicators for more sensitive monitoring and management of coastal groundwater resources. Full article

The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuronal networks, predict connectivity patterns, and examine how morphological changes impact neural network function. In this study, we aimed to explore the role of the actin cytoskeleton in neuronal signaling via primary cilia and to elucidate the role of the actin network in conjunction with neuronal electrical activity in shaping spatial neuronal formation and organization, as demonstrated by relevant mathematical models. Our proposed model is based on the polygamma function, a mathematical application of ramification, and a geometrical definition of the actin cytoskeleton via complex numbers, ring polynomials, homogeneous polynomials, characteristic polynomials, gradients, the Dirac delta function, the vector Laplacian, the Goldman equation, and the Lie bracket of vector fields. We were able to reflect the effects of neuronal electrical activity, as modeled by the Van der Pol equation in combination with the actin cytoskeleton, on neuronal morphology in a 2D model. In the next step, we converted the 2D model into a 3D model of neuronal electrical activity, known as a core-shell model, in which our generated membrane potential is compatible with the neuronal membrane potential (in millivolts, mV). The generated neurons can grow and develop like an organoid brain based on the developed mathematical equations. Furthermore, we mathematically introduced the signal transduction of primary cilia in neurons. Additionally, we proposed a geometrical model of the neuronal branching pattern, which we described as ramification, that could serve as an alternative mathematical explanation for the branching pattern emanating from the neuronal soma. In conclusion, we highlighted the relationship between the actin cytoskeleton and the signaling processes of primary cilia. We also developed a 3D model that integrates the geometric organization unique to neurons, which contains soma and branches, such that the mathematical model represents the interaction between the actin cytoskeleton and neuronal electrical activity in generating action potentials. Next, we could generalize the model into a cluster of neurons, similar to an organoid brain model. This mathematical framework offers promising applications in artificial intelligence and advancements in neural networks. Full article

In recent years, the continuous development of deep learning has significantly advanced its application in the field of remote sensing. However, the semantic segmentation of high-resolution remote sensing images remains challenging due to the presence of multi-scale objects and intricate spatial details, often leading to the loss of critical information during segmentation. To address this issue and enable fast and accurate segmentation of remote sensing images, we made improvements based on SegNet and named the enhanced model CSNet. CSNet is built upon the SegNet architecture and incorporates a coordinate attention (CA) mechanism, which enables the network to focus on salient features and capture global spatial information, thereby improving segmentation accuracy and facilitating the recovery of spatial structures. Furthermore, skip connections are introduced between the encoder and decoder to directly transfer low-level features to the decoder. This promotes the fusion of semantic information at different levels, enhances the recovery of fine-grained details, and optimizes the gradient flow during training, effectively mitigating the vanishing gradient problem and improving training efficiency. Additionally, a hybrid loss function combining weighted cross-entropy and Dice loss is employed. To address the issue of class imbalance, several categories within the dataset are merged, and samples with an excessively high proportion of background pixels are removed. These strategies significantly enhance the segmentation performance, particularly for small-sample classes. Experimental results from the Five-Billion-Pixels dataset demonstrate that, while introducing only a modest increase in parameters compared to SegNet, CSNet achieves superior segmentation performance in terms of overall classification accuracy, boundary delineation, and detail preservation, outperforming established methods such as U-Net, FCN, DeepLabv3+, SegNet, ViT, HRNe and BiFormert. Full article

Moisture–electricity generators (MEGs) hold great promise for green energy conversion. However, existing devices focus on the need for complex gradient distribution treatments and the improvement in output voltage, overlooking the important role of the graphene oxide (GO) oxidation degree and the response time and recovery time in practical application. In this work, we develop printed MEGs by synthesizing reduced graphene oxide/silver nanoparticle (rGO/Ag) composites and controlling the GO oxidation degree. The rGO/Ag layer serves as a functional component that enhances cycling stability and shortens the recovery time. Additionally, compared to conventional rigid-structure devices, these flexible MEGs can be produced by inkjet printing and drop-casting techniques. A 1 cm² MEG can generate a voltage of up to 60 mV within 2.4 s. Notably, higher output voltages can be easily achieved by connecting multiple MEG units in series, with 10 units producing 200 mV even under low relative humidity (RH). This work presents a low-cost, highly flexible, lightweight, and scalable power generator, paving the way for broader applications of GO and further advancement of MEG technology in wearable electronics, respiratory monitoring, and Internet of Things applications. Full article