Search Results (2,321)

Nanofiber-based structures have shown considerable potential in semiconductor-related applications, including ultra-thin dielectric layers and flexible electronic devices, owing to their tunable micro-/nanoscale morphology. However, the manufacturing of these structures is often hindered by the complex multiparameter coupling and poor reproducibility inherent in conventional electrospinning processes. To address these challenges, this study develops an intelligent optimization framework for gas-assisted electrospinning by integrating Large Language Models (LLMs) with Bayesian Optimization (BO). A Gaussian Process Regression (GPR) surrogate model was established to navigate the high-dimensional parameter space efficiently. Comparative studies demonstrate that the proposed BO+LLM strategy not only outperforms pure data-driven BO and pure knowledge-driven LLM approaches but also surpasses the conventional Response Surface Methodology (RSM) baseline, successfully locating a verified minimum fiber diameter of 239 nm. Furthermore, through response-surface analysis, this work identifies a specific multiphysics collaborative window where electrostatic stretching and aerodynamic assistance are balanced. These findings provide a robust pathway for the reproducible fabrication of nanofiber-based electronic devices. Full article

As human space missions become longer and more autonomous, robots are expected to assume broader responsibilities in inspection, maintenance, logistics, scientific support, and crew assistance. Among available robot forms, humanoid robotic astronauts are especially relevant because their anthropomorphic embodiment is compatible with human-centered habitats, tools, interfaces, and procedures. Their deployment in orbital and planetary environments, however, introduces challenges that differ from those of terrestrial humanoids, including floating-base dynamics, intermittent contact, whole-body coordination, constrained perception, and delayed supervision. This review contributes a mission-oriented and astronaut-centered synthesis of humanoid robotic astronauts, distinguishing itself from platform-by-platform or morphology-only surveys. It treats these systems as mission-compatible embodied agents whose feasibility depends on the coupling among mission context, morphology, contact behavior, perception, autonomy, and validation evidence. The primary goals are threefold: to classify representative platforms according to mission context, to synthesize the core technical foundations required for mission-compatible operation, and to identify cross-cutting deployment bottlenecks and benchmarking priorities for future development. Representative systems are organized into intravehicular assistance, extravehicular operations and on-orbit servicing, and surface exploration or transitional scenarios, showing how mission demands shape embodiment, mobility, manipulation, autonomy, and validation strategies. This review further summarizes recent progress in microgravity dynamics and contact mechanics, multimodal perception and scene understanding, whole-body motion planning and control, teleoperation and supervised autonomy, and evaluation and benchmarking methods. The analysis indicates that humanoid robotic astronauts are not simple extensions of terrestrial humanoids but astronaut-oriented embodied systems for mission-constrained environments. Three priorities are identified for future development: contact-rich whole-body intelligence under support transitions, delay-tolerant supervised autonomy with explicit authority handoff, and systematic benchmarking pipelines that connect simulation, ground analogs, short-duration microgravity tests, human-in-the-loop trials, and mission-context demonstrations. Full article

While aluminum (Al) continues to be a cornerstone for microelectronic interconnect technologies, its chronic tendency toward hillock growth and thermal instability necessitates a transition toward high-performance nanostructured material architectures. This research tackles these reliability bottlenecks by achieving a molecular-level integration of graphene nanoplatelets (GNPs) within Al matrices, a strategy designed to fortify structural resilience. Adopting a green chemistry approach, we synthesized Al-GNP (0.25 vol.%) composite thin films through Pulsed Laser Deposition (PLD) using precursors derived from recycled aluminum. A major obstacle—the formation of the deleterious Al₄C₃ intermetallic phase—was effectively suppressed by ensuring a homogeneous supramolecular dispersion via a specialized dual protocol (ultrasonication and magnetic stirring) during the powder metallurgy stage. Comprehensive physicochemical characterization, utilizing HR-TEM and XRD, verified the structural integrity of the multilayer GNPs (d-spacing = 4.6 Å). Furthermore, surface metrology analysis uncovered a radical shift in growth kinetics: whereas pure Al grew via a “spiky” Volmer-Weber mechanism (Sku = 31.17), the carbon-based inclusion stabilized the film evolution, tempering the kurtosis to Sku = 7.74. Analytical cross-sectional EDS confirmed both stoichiometric fidelity and the achievement of void-free Si/Pt/Al-GNP interfaces. These outcomes prove that a precise nanoscale tailoring of surface morphology via carbonaceous reinforcements significantly bolsters microstructural stamina. Consequently, these PLD-deposited composites emerge as sustainable, cutting-edge candidates for the next generation of microelectronic packaging and interfacial chemistry applications. Full article

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning. Full article

This study explored the regulatory role of nuclear factor E2-related factor 2 (Nrf2) in aerobic exercise improving oxidative stress and inflammatory responses in mice with insulin resistance (IR) induced by a high-fat diet. We established an IR mouse model through a high-fat diet, then subjected the IR mice to aerobic exercise, intraperitoneal injection of luteolin, or a combined intervention. After 6 weeks of intervention, we measured serum lipid and glucose profiles; evaluated skeletal muscle morphology by H&E staining; quantified mRNA expression levels of Nrf2 and its downstream targets in the skeletal muscle by RT-qPCR; and determined protein abundance, localization, and expression patterns of Nrf2 and NOD-like receptor protein 3 (NLRP3) inflammasome by Western blotting and immunohistochemistry, respectively. In the skeletal muscle of IR mice, Nrf2 and its downstream targets were significantly down-regulated, whereas NLRP3 inflammasome was markedly up-regulated (p < 0.05 or p < 0.01). IR mice subjected to aerobic exercise exhibited reduced serum glucose and lipid levels together with a lower insulin-resistance index (p < 0.05 or p < 0.01); morphologically, inter-myofibrillar spaces were narrowed, intrafiber vacuoles diminished, and cellular integrity restored. Concomitantly, Nrf2 and its downstream targets were up-regulated, whereas NLRP3 inflammasome components were down-regulated in the skeletal muscle (p < 0.05 or p < 0.01). Intraperitoneal administration of luteolin during exercise, however, partially attenuated or reversed these exercise-induced improvements by inhibiting the activation of Nrf2 (p < 0.05 or p < 0.01). These results indicate that aerobic exercise confers protective effects against IR by activating the Nrf2 signaling pathway, thereby attenuating oxidative stress and inflammation; these benefits are markedly attenuated when Nrf2 activity is pharmacologically inhibited. Full article

Selective laser melting (SLM) offers significant potential for fabricating lightweight 316L stainless steel lattice structures (LSs), while forming defects and microstructural heterogeneity remain challenging, especially in fine struts. In this study, response surface methodology (RSM) and analysis of variance (ANOVA) were employed to quantify the coupled effects of geometric parameters (forming angle, FA; rod diameter, RD) and processing parameters (laser power, LP; scanning speed, SS; hatch spacing, HS) on the mass deviation (MD) of fine struts. The results show that FA and RD are the dominant factors affecting MD within the investigated parameter range, whereas LP and SS exhibit comparatively weaker effects. Representative samples with different FA and RD were further characterized by SEM, XRD, and EBSD to examine the associated microstructural evolution. The observations indicate that changes in FA and RD are accompanied by variations in solidification morphology, defect distribution, crystallographic texture, and GND density. Higher FA is associated with lower MD and stronger texture alignment along the building direction, whereas larger RD tends to promote columnar growth and enhanced texture intensity. These results suggest that geometric parameters can serve as effective design variables for tailoring forming deviation and representative microstructural characteristics of fine struts in SLM-fabricated 316L lattice structures. Full article

Urban design that improves microclimate can significantly enhance the ecological livability of human settlements, while the climate-adaptive wisdom of applying local water-net landscapes to modern urban renewal requires further validation. To investigate the optimization mechanism of waterscape on microclimate comfort, this study focuses on the public space of Xinsheng District in the Suzhou water-net region. By integrating continuous incremental multi-scenario form design, computational fluid dynamics (CFD) multi-physics simulation, and climate sensation evaluation, we reproduce the spatial differentiation of microclimate and comfort gradients across multi-hour periods during hot summer daytime within the built-up environment involving waterbodies, vegetation, and buildings. Consequently, an indicator of comfort improvement efficiency (CIE) is proposed to measure the spatial effectiveness of per-unit-area water surface expansion on climate sensation. Results show that when controlling other morphological parameters and designing three incremental waterbody scenarios—no water surface, 50% water, and 100% waterscape—the relative comfort area expanded across all time periods as water increased. This implies that waterscape variations exert a positive effect on microclimate suitability. However, during the expansion of water area at each time, the CIE was higher in the 0–50% initial stage of water surface increase compared to the 50–100% later morphological stage. Therefore, this study reveals the stepwise nonlinear trend by which increased water area in the built-up environment improves the climate suitability of waterfront spaces. Furthermore, under constraints of equivalent area and other geometric forms, a more dispersed and networked waterscape was found to be a superior spatial strategy. This confirms the microclimate wisdom of the water-net landscape in the Jiangnan locality, providing form optimization guidance for ecologically oriented urban renewal design. Full article

To address the accuracy limitations in identifying micro-scale and low-distinguishability defects, we proposes an improved EfficientNet model for wafer defect classification in semiconductor fabrication. In particular, we construct the model using EfficientNetV2 architectures as the backbone and introduce a multi-scale self-attention enhancement module to strengthen the capture capability for critical defect characteristics. This module consists of four parallel self-attention enhancement modules, aiming to obtain spatial context information at different levels and enhance relevant features through a self-attention mechanism. Meanwhile, we merge the manually extracted features of defects with the CNN’s fully connected layer, effectively compensating for the deficiency of automatic features in the differentiated representation of defects. The manual feature extraction module leverages image processing techniques to capture diverse morphological characteristics of defects including geometric features, moment features and texture features. We simulate and generate a lithography SEM image dataset with various types of defects based on the typical line-space structure and the ICCAD2019 mask pattern dataset. The total sample size of the wafer defect dataset is 1500, covering 15 typical defects with an average distribution. The classification performance of models is evaluated on the simulated defect dataset. The results indicate that the overall classification accuracy of the improved model reaches 96.60%, representing an improvement of 8.14% compared to the original EfficientNetV2. This demonstrates the superiority of the proposed model in addressing classification tasks involving micro-scale and low-distinguishability defects. Full article

Spatial transcriptomics (ST) has emerged as an essential technology for interpreting the molecular profiles underlying pathological tissue morphology. Most existing ST analyses are limited to 2D sections, which ignore the complex structural and molecular heterogeneity of biological tissues in 3D space and may cause diagnostic oversights. Since acquiring complete 3D ST volumes is resource-intensive, recent 3D imputation paradigms provide a cost-effective alternative by integrating 3D whole-slide images (WSIs) with sparse 2D ST references (e.g., a single slide). Despite this methodological advancement, effectively modeling complex cross-layer spatial dependencies remains challenging. Current mainstream solutions predominantly adopt standard Transformers for cross-scale feature aggregation, which may bring computational overhead and higher overfitting risk while having limited explicit mechanisms for hierarchical anatomical guidance. To address these limitations, we propose a Hierarchical MambaOut-based Spatial Imputation Graph Network (HM-ASIGN) for anatomy-aware 3D spatial transcriptomics imputation. Our architecture leverages MambaOut’s dynamic gated 1D convolutions as a parameter-efficient alternative to dense global self-attention. This design captures the depth-wise evolution of pathological features while reducing over-parameterization. Inspired by the macro-to-micro diagnostic reasoning of clinical pathologists, HM-ASIGN introduces a multi-scale recursive guidance mechanism. It constructs a top-down information flow by extracting global anatomical priors at macroscopic scales and injecting them as contextual anchors into regional and spot-level features in a cascaded manner. This helps ensure that fine-grained molecular predictions are properly constrained by global morphological structures. Evaluation experiments on multiple public breast cancer datasets demonstrate that HM-ASIGN achieves competitive reference-level performance against existing baselines, reaching a Pearson Correlation Coefficient (PCC) of 0.772. Specifically, when evaluated against the foundational ASIGN framework, it improves predictive accuracy while reducing the total parameter count by approximately 33.3% and improving inference throughput. Our results suggest that HM-ASIGN provides a computationally efficient approach for 3D spatial molecular mapping. Full article

Reducing cement consumption in mortar systems is essential for lowering the environmental impact of cement-based materials. Conventional mix design approaches rely mainly on particle size distribution and fineness modulus, which do not fully capture the effects of aggregate packing, morphology, and petrographic composition on paste demand and mechanical performance. Fourteen fine aggregates of distinct geological origins were experimentally characterized in terms of physical and petrographic properties. A dataset of 211 mortar mixtures, yielding 633 transverse-strength observations, was used to train a Random Forest Regressor (RFR) model for strength prediction. The model achieved $R^2=0.762$ (RMSE = 0.223 kN; MAE = 0.165 kN), demonstrating its reliability as a surrogate screening tool. This study presents a hybrid framework that integrates particle packing theory with machine learning to optimize fine aggregate blends. By introducing a Paste Demand Index (PDI)—combining normalized uncompacted void content, surface texture, and shape—the framework enables the identification of mixtures that minimize paste demand while maintaining mechanical performance under strength constraints. Results confirm that the proposed PDI and strength-based filtering are robust, offering a physically grounded decision-support methodology for narrowing the design space. Ultimately, this approach provides an efficient strategy for resource optimization, effectively bridging the gap between computational screening and laboratory validation in cement-reduction initiatives driven by the cement-based tile manufacturing industry. Full article

With the rising interest in hydrogen technologies as a pathway toward lower-carbon energy systems, there is a growing need for proton exchange membranes that can operate reliably in the 120–200 °C window. This second part of the review examines mixed phosphate–silicate networks, composites, and hybrid membranes designed to move beyond the limitations of the single-anion glasses discussed in Part I. Rather than listing compositions only, the present analysis is organized around a comparative framework that links network chemistry, hydration management, pore-space morphology, interfacial proton transport, and durability under thermal/humidity cycling. Mixed-anion lattices, sol–gel-derived porous glasses, polymer-assisted interpenetrating networks, ionic-liquid-modified systems, fully inorganic composites, and mechanochemically prepared hybrids are evaluated with respect to conductivity, humidity tolerance, structural stability, and device relevance. Particular attention is paid to strategies that attempt to decouple proton conductivity from simple water uptake by combining acidic-site engineering with mesostructural control. The literature shows that recent progress is real but uneven. Conductivity gains are often achieved through better retention of hydrated proton pathways or acid-rich interphases, yet these benefits remain constrained by pore collapse, acid migration, gas crossover, interfacial losses, or insufficient long-term validation in membrane–electrode assemblies. The review, therefore, closes with a cross-class benchmarking matrix and a synthesis-oriented guide intended to support more critical comparison of future intermediate-temperature membrane designs. Full article

As universities strive for socially sustainable environments, Informal Learning Spaces (ILS) serve as vital social infrastructure. However, previous studies often isolate physical environmental stimuli from internal psychological decision-making and treat harsh climates as absolute barriers. To address this gap, this study integrates Environment–Behavior Studies (EBS) and the Theory of Planned Behavior (TPB) to construct a comprehensive behavioral model for ILS in hot–humid climates. Using Structural Equation Modeling on 377 samples from Guangzhou, China, the study quantifies the interaction between physical spatial affordances and internal psychological mechanisms. The results reveal a critical shift in behavioral drivers: when psychological agency is introduced, the driving force of high-quality Space Design (path coefficient = 0.269) surpasses the restrictive impact of the severe Climate Environment (coefficient = 0.218). This demonstrates that architectural affordances can actively buffer physiological discomfort. Internally, Perceived Behavioral Control (PBC)—acting as an empirical proxy for user agency—emerges as the sole psychological dimension directly driving actual spatial usage (coefficient = 0.131), whereas personal attitudes and peer pressure show no significant direct behavioral impact. Furthermore, the direct behavioral influence of operations management becomes non-significant when mediated by psychological expectations. Ultimately, this study reframes ILS optimization, demonstrating that socially sustainable campus revitalization in hot–humid regions must prioritize empowering user autonomy and enhancing robust morphological design over administrative upgrades or mere passive climate endurance. Full article

K-ion batteries (KIB) are considered the future energy storage and conversion technology due to their remarkable performance. In this work, a high-temperature solid-state process was used to effectively synthesize KMnO₂, a promising cathode material for KIBs. The materials were examined using X-ray powder diffraction (XRPD), Raman and infrared spectroscopies, electron microscopy analysis, optical, and impedance spectroscopies. Rietveld refinement of X-ray diffraction data confirmed that the compound crystallizes in the monoclinic system with the P-2₁/m space group. Fourier transform infrared and Raman spectroscopies revealed the vibrational modes of the KMnO₂ compound and proved the existence of the octahedral environment MO₆ (M = Mn, K), which affirms structural configuration. The morphological distribution and grain size of the titled compound were examined using SEM studies. A direct band gap of around 3.12 eV was found by optical studies using UV–Vis spectroscopy, confirming the semiconducting nature of KMnO₂ and indicating its applicability for optoelectronic and energy-related applications. The characteristics of this material were further examined using impedance spectroscopy at temperatures between 343 and 443 K and a frequency range of 10⁻¹ Hz to 10⁶ Hz. The DC conductivity and relaxation time exhibited Arrhenius behavior, with a significant shift in activation energy at 373 K, suggesting a change in the conduction mechanism. The frequency behavior of AC conductivity, σ_ac, was analyzed using the universal Jonscher law. The findings of the charge transportation study on KMnO₂ indicate that this material follows a non-overlapping small polaron tunneling (NSPT) for T < 383 K and correlated barrier hopping (CBH) above for T > 383 K. A correlation between the ionic conductivity and the crystal structure was established and discussed. Full article

Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, as strided operations discard critical spatial details essential for the localization of tiny objects. To address these issues, we propose RFE-YOLO, a lightweight receptive field-enhanced network specifically tailored for high-precision small object detection in UAV scenarios. First, the Cross-Scale Receptive Field Enhancement (CSRE) module is designed to mitigate intrinsic information loss by integrating space-to-depth convolution (SPD-Conv), which preserves spatial details by migrating them into the channel dimension. This module further employs an energy-based adaptive weight generation mechanism to distinguish target signals from environmental noise. Second, this paper proposes the C3k2-Dynamic Inception Mixer Block (C3k2-DIMB), which adaptively captures anisotropic features—such as slender vehicles—via dynamic kernel weighting and multi-shape inception kernels. Third, the Shuffled Upsampling for Resolution Enhancement (SURE) module is introduced to maintain spatial fidelity during resolution recovery, utilizing a channel shuffle mechanism to overcome information isolation. Finally, the Multi-feature Fusion Module (MFM) replaces conventional static concatenation with a dynamic softmax-based competition mechanism, effectively bridging the semantic gap between multi-level features while suppressing background distractors. Experimental results on the VisDrone dataset demonstrate that RFE-YOLO significantly enhances the representation capability for small objects. Specifically, the proposed model achieves a state-of-the-art $mA{P}_{50}$ of 42.70%, representing a substantial 9.3% improvement over the baseline YOLO11n. Furthermore, our architecture maintains an exceptionally lightweight profile with only 1.91 M parameters, demonstrating that high-precision detection can be achieved through structural intelligence rather than excessive parameter scaling. This makes RFE-YOLO highly suitable for real-time inference on edge-deployed UAV platforms. Full article

Unconventional aircraft configurations are considered as potential solutions to achieve the ambitious emission reduction goals in aviation. However, the identification, selection, and synergetic combination of promising technologies remain a highly vague and uncertain process. This has been addressed in the framework for the advanced morphological approach (FAMA), which represents a structured design process for the generation and evaluation of unconventional aircraft configurations. It implies the decomposition of the task into subproblems, their analysis and the synthesis of concepts in a solution space. This general workflow has been further developed and adapted on three levels in aircraft design: (1) the qualitative idea generation; (2) the semi-quantitative concept selection from the generated ideas; and (3) the probabilistic estimation of design parameters and figures of merit for the most promising concepts from the previous level. The current paper focuses on the overview of the finalized methodology as well as levels one and two, while level three will be presented in more detail in future work. The first level is demonstrated on the concept generation for regional aerial transportation. The second level results in the percentual performance comparisons of promising technologies for the design of an energy-efficient long-range aircraft. Full article