Search Results (1,227)

To investigate the effect of microalloying on hydrogen embrittlement resistance of hot-stamped steels with strength levels of 1.8 GPa and above, six composition schemes were designed based on conventional 34MnB5 steel, including three routes, namely Nb, V, and Nb–V. U-bend constant-strain bending tests and slow strain rate tensile (SSRT) tests were conducted on quenched specimens for each scheme. Results indicated that the Nb-containing compositions exhibited superior hydrogen embrittlement resistance. The mechanism by which microalloying refines the martensitic microstructure of 34MnB5 in the quenched state and enhances its resistance to hydrogen embrittlement was studied in detail. It was found that Nb exhibits stronger effects than V in refining and homogenizing the martensite structure. The fundamental reasons for Nb’s enhanced ability to pin austenite grain boundaries at high temperatures—leading to better microstructural refinement and homogenization—are its higher temperature range for second-phase precipitation, greater driving force for grain boundary diffusion, lower austenite grain boundary diffusion coefficient, and weaker tendency for high-temperature coarsening of precipitates. The microstructural refinement and homogenization induced by Nb addition are more pronounced than those achieved by combined additions of Nb and V. Furthermore, within the concentration range of 0–0.1%, the amount of Nb is positively correlated with the degree of microstructural refinement and homogenization. By reducing martensite lath size through microalloying, multiple microstructural modifications occur: decreased density of geometrically necessary dislocations (GNDs) in the matrix, significant increase in interface density—especially a higher proportion of high-angle grain boundaries—reduced number of Σ3 special harmful grain boundaries, weakened matrix texture intensity, fewer twin martensites, and smaller twin martensite sizes. These factors collectively contribute significantly to the improved hydrogen embrittlement resistance of Nb-containing steels. Full article

In this study, we developed two complementary synthetic routes to novel piperidine- and azetidine-containing 2,3-disubstituted propanoic acid derivatives as heterocyclic amino acid building blocks. The strategy employs ketone- and carboxylic acid-derived Meldrum’s acid intermediates, which are converted into common α,β-unsaturated methyl esters through methanolysis; they are subsequently diversified via DBU-promoted aza-Michael addition with saturated cyclic amines and aromatic NH-heterocycles. The ketone-derived approach provided piperidine-containing derivatives in 35–89% yield and the corresponding azetidine analogues in 61–92% yield, whereas the complementary acid-derived route afforded regioisomeric products in 59–85% and 61–89% yield, respectively. Both synthetic sequences tolerated a broad range of nitrogen nucleophiles, and no alternative regioisomeric aza-Michael products were detected for heterocycles containing multiple nitrogen atoms. The structures of the synthesized compounds were established via ¹H, ¹³C, ¹⁵N, and ¹⁹F NMR spectroscopy together with HRMS, including detailed multidimensional NMR analysis of representative products. The developed methodology provides efficient access to structurally diverse heterocyclic propanoic acid derivatives and expands the repertoire of amino acid building blocks available for peptide chemistry, medicinal chemistry, and DNA-encoded library synthesis. Full article

Mixed-viewpoint pavement crack detection remains challenging because aerial (Drone) and ground-level (Ground) images exhibit substantially different feature distributions, while repeated downsampling inevitably weakens the representation of fine crack structures in UAV (unmanned aerial vehicles) imagery. To address these issues, this study proposes VCDH-Net, a lightweight mixed-viewpoint crack detection framework built upon YOLOv11n. The framework introduces a Viewpoint-Conditioned Dual-Head Detection (VCDH) architecture that dynamically routes features to viewpoint-specific detection heads through a lightweight viewpoint classifier, enabling specialized optimization while maintaining a shared feature extraction backbone. On this basis, a Lightweight Structure Enhancement (LSE) module is incorporated into mid-level feature layers to reinforce directional crack structures by exploiting local contrast and geometric priors. Furthermore, a Pyramid Detail Refinement (PDR) module is developed for the Drone branch to recover fine-grained spatial information of ultra-small cracks through a lightweight upsample–refine–downsample residual pathway. To provide a more comprehensive evaluation of mixed-viewpoint detection performance, a cross-view assessment framework is further established by introducing three complementary metrics, namely Cross-View Gap (CV-Gap), Worst-View Score (VWS), and Cross-View Balance (CVB). Experiments conducted on a dual-viewpoint pavement crack dataset collected from roads in and around Nanjing, China demonstrate that the proposed method achieves an mAP50-95 of 57.88%, improving the baseline YOLOv11n by 1.55 percentage points. Meanwhile, the Drone-view mAP50-95 increases from 46.98% to 48.83%, and the proposed framework attains the highest CVB score of 0.4965, indicating improved cross-viewpoint detection consistency under the tested conditions. These results, validated on a single-region dataset, demonstrate that VCDH-Net effectively alleviates viewpoint-induced feature discrepancies on the tested data while enhancing the representation of fine crack structures. Generalization to other geographic regions requires further validation on multi-viewpoint datasets not yet publicly available. Full article

Two-dimensional transition-metal carbides and nitrides (MXenes) are increasingly adopted in advanced electronic devices, where their metallic conductivity, optical tunability, and chemically addressable surfaces support next-generation multifunctional optoelectronics. Their practical performance, however, depends not only on their intrinsic properties but also on the heterogeneous interfaces where charges, photons, and ions interact. Unlike earlier reviews organized around synthesis routes or separate device categories, this review takes interfacial chemistry as a single organizing principle and follows it from surface terminations through to integrated systems. The structural and surface-chemical characteristics of MXenes are described first, showing how dynamic terminations and interfacial dipoles regulate work functions and energy-level alignment. We then discuss molecular functionalization, defect passivation, and heterojunction formation as strategies for reducing Schottky barriers and improving charge-transfer kinetics. Optoelectronic platforms built on these engineered interfaces, including high-efficiency photovoltaics, broadband photodetectors, and stretchable wearable systems, are subsequently detailed, together with emerging architectures that merge self-powered sensing with neuromorphic visual functions, a scope seldom treated alongside conventional devices in previous surveys. By connecting surface chemistry with device integration, this review outlines a materials-to-systems pathway toward more reliable and scalable MXene-based optoelectronic technologies. Full article

This study addresses the critical need for accurate and efficient large-scale urban fire spread path prediction in dense urban areas by proposing a new gravitational framework-based theory. Its core innovation is the “characteristic attractive force” model, which mechanistically quantifies fire spread as a dynamic interaction between buildings, integrating factors like spacing, height, area and density effects to predict trajectories from the initially ignited building. This study adopts a GIS-based rapid prediction framework that circumvents the dependence on complex physical parameters. It utilizes high-precision spatial data and optimized algorithms to streamline prediction processes while retaining favorable prediction accuracy. Validated on two real-world clusters, the proposed approach enables effective visualization of dynamic propagation trajectories and pathway spectra that characterize the detailed propagation routes and ignition sequences. Notably, the framework achieves exceptional efficiency, completing predictions for large clusters in tens of seconds per scenario, making it suitable for real-time risk assessment. Overall, this work advances urban fire modeling with an innovative, efficient, and practical tool to support fire safety engineering and emergency management decision-making. Full article

Pyrolysis is a leading route for valorizing lignocellulosic residues, yet detailed multi-step kinetic schemes have so far been deployed only in costly commercial simulators, limiting reproducibility. This work couples thermogravimetric (TGA) characterization with process simulation in the free, open-source simulator DWSIM to predict the pyrolysis product distribution of corn cob from Córdoba, Colombia. The lignocellulosic composition (hemicellulose 24.3 ± 2.9, cellulose 36.4 ± 3.0, lignin 39.3 ± 0.9 wt%) was obtained by deconvolving the derivative thermogravimetric (DTG) curve with a five-parameter asymmetric double sigmoidal (Asym2sig) function (R² > 0.9996). Pseudocomponent activation energies from the Coats–Redfern method (154.2, 124.6, and 29.9 kJ/mol) calibrated the primary reactions of a 17-reaction Ranzi scheme, extended with 18 secondary gas-phase steam reforming reactions. Validated against eight lignocellulosic biomasses, the calibrated model yielded a consolidated R² = 0.853 and average absolute deviation (AAD) = 9.8%, with char predictions most accurate (AAD = 8.9%). For corn cob, a bio-oil-optimized yield of 55.0 wt% was predicted at 500 °C, transitioning to a syngas-rich regime (51.0 wt% gas) at 750 °C. This constitutes the calibrated Ranzi-scheme implementation in DWSIM, offering an accessible, reproducible pathway for biomass pyrolysis modeling. Full article

Under globalization, architectural forms, spatial logic, and consumption experiences have become increasingly homogeneous, making placelessness particularly evident in large-scale commercial complexes. Establishing meaningful connections between globalized commercial spaces and local culture, urban memory, and everyday life has consequently become an important issue in research on architectural cultural sustainability. Guided by critical regionalism, this study takes placelessness as its point of departure and cultural sustainability as its evaluative perspective. It develops a two-level analytical framework comprising architectural analysis and user perception evaluation and applies this framework to the Chengdu New Century Global Center. Architectural analysis examines the building’s objective characteristics in terms of spatial image, functional organization, environmental setting, and cultural expression. User perception data were collected through a questionnaire survey and semi-structured interviews, followed by descriptive statistics and comparative analysis across four corresponding dimensions: spatial, functional, environmental, and cultural perception. The results indicate that the building’s megascale form, contemporary architectural appearance, and multifunctional organization contribute to higher levels of spatial and functional perception. Environmental perception is also relatively positive, although deficiencies remain in resting facilities, route organization, and detailed spatial experience. By contrast, cultural perception is substantially lower than the other dimensions. The marine theme, exoticized settings, and entertainment-oriented imagery are more closely associated with a globalized consumer landscape than with the spatial translation of Chengdu’s local culture, urban memory, and everyday life. Consequently, the building’s thematic identity has not been effectively translated into users’ recognition of place-specific characteristics, suggesting that the production of commercial complex space continues to prioritize consumption efficiency over cultural expression. By operationalizing the critical regionalist concept of sense of place as an empirically assessable analytical tool, this study extends its applicability to large-scale commercial complexes and provides a diagnostic framework and practical strategies for enhancing their cultural sustainability. Full article

Low-light image enhancement (LLIE) seeks to improve visibility while suppressing amplified noise, preserving color, and preventing highlight over-enhancement. Most existing methods infer a normally exposed image from a single observed representation, requiring one feature stream to reconcile shadow brightening, highlight protection, denoising, and chromatic correction. We propose VEX, a chroma-stable virtual exposure routing network for single-image LLIE. A Chroma-Stable Virtual Exposure Generator (CVEG) decomposes the input into luminance and log-chroma components and produces five learnable virtual exposure states by perturbing luminance in a bounded space under a shared log-chroma constraint. A shared multi-scale encoder extracts comparable features from all states. At each of four scales, a Noise-Saturation-aware Exposure Router (NSER) combines learned feature evidence with explicit luminance, mid-tone, saturation, and detail-variation priors to predict pixel-wise softmax weights over the exposure states. An Exposure State Mixer (ESM) then performs gated multi-view recalibration of the routed bottleneck, after which a routed-skip decoder predicts a residual correction. VEX therefore casts LLIE as spatially adaptive selection among internal exposure hypotheses rather than as direct single-state regression. Extensive experiments on CDD-11 and LOL demonstrate that VEX consistently outperforms representative traditional and learning-based methods across fidelity, structural, and perceptual criteria. Full article

Existing vectored immunoprophylaxis (VIP) approaches have primarily focused on IgG, which provides systemic protection but is less specialized in mucosal immunity. In contrast, secretory IgA (sIgA) plays a central role at epithelial surfaces, promoting pathogen neutralization while limiting inflammation. Although monoclonal IgA therapies are effective, their short half-life requires repeated dosing. Thus, VIP strategies enabling sustained sIgA expression at mucosal sites could transform mucosal infection prevention and treatment. This review outlines the key challenges associated with in vivo IgA expression and discusses critical considerations for VIP-mediated IgA delivery at mucosal surfaces, with emphasis on its potential for clinical translation. We provide a detailed overview of platforms for targeted IgA expression, including adeno-associated virus (AAV), adenoviral and lentiviral vectors, and lipid nanoparticle-based systems, alongside relevant routes of administration. Additionally, we examine emerging strategies to enhance the robustness, durability, and localization of IgA expression in vivo. Overall, VIP-enabled IgA expression represents an emerging strategy for enhancing mucosal immunity, with continued advances required to establish its role in the prevention and treatment of mucosal infections. Full article

High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automated vegetation monitoring, existing models still face challenges when a common segmentation architecture is evaluated under different imaging geometries. In this study, Cityscapes and ISPRS Vaihingen are treated as two independent benchmarks representing perspective street-level imagery and orthographic aerial imagery, rather than as simultaneous cross-view inputs. “Background dominance” caused by perspective distortion and the “gridding artifacts” inherent in orthographic textures severely constrain segmentation accuracy across varying vegetation scales, particularly for small targets. To address these limitations, we propose a Scale-Aware Mixture of Experts (SA-MoE) architecture for fine-grained vegetation segmentation under two distinct imaging views, together with a scene-specific optimization strategy. The core SA-MoE framework consists of two main components. First, the spatial gating network uses a temperature polarization mechanism with τ = 0.5 to adjust the initial logit maps, sharpening expert-weight differences while preserving stable gradient propagation. Second, we use a heterogeneous expert group with five parallel branches: a pixel-level expert, three spatial experts with different dilation rates, and a global average-pooling expert. A dynamic pixel-level weighted fusion mechanism is then applied, decoupling feature extraction from receptive-field allocation. Furthermore, to address the heterogeneity of “hard samples” and “label noise” across the two benchmark settings, we introduce a scene-specific optimization strategy. Our findings show that the Focal-Dice (FD) loss is more suitable for perspective scenes with severe target imbalance and hard-to-classify vegetation targets, whereas the Cross-Entropy (CE) loss is more robust to boundary jitter in orthographic imagery. Comparative experiments on the Cityscapes (perspective view) and ISPRS Vaihingen (orthographic view) datasets reveal that SA-MoE achieves a highly competitive balance between computational efficiency and fine-grained segmentation, particularly in micro-target recall. Notably, the recall for extra-small (XS) scale targets in the aerial dataset improved by 3.21 percentage points compared to the second-best model. For the street-level dataset, our model achieved competitive global performance in terms of Overall Accuracy (OA), Precision, and F1-Score. However, we also observed a performance trade-off, where Transformer-based models maintained an advantage in preserving fine boundary details for these extra-small targets. In the routing analysis, we observed a pattern that we refer to as “receptive field inversion”, in which the model assigns lower weights to large-dilation experts for large canopy regions in orthophotos. We interpret this pattern as a plausible routing hypothesis. Overall, SA-MoE offers an efficient and adaptive solution for urban vegetation mapping under two imaging views. Full article

Driven by low-carbon mandates, hybrid power vessels integrating diesel, battery, shore-power, and photovoltaic vessel (PV) systems are emerging as a key green shipping pathway. However, operation scheduling is essentially complex due to the integration between supply-side routing and load-side energy dispatch, which is compounded by multi-dimensional uncertainties in PV generation, port-grid loads, and feeder delays. To address this, we formulate a unified two-stage robust optimization model. The objective is to simultaneously minimize operating costs and enhance port-grid friendliness by coordinating on-board energy management and shore-power interactions. In detail, the first stage determines routing and sailing speeds, while the second stage allocates multi-source power under a worst-case budgeted polyhedral uncertainty set. A piecewise-linearization scheme handles the cubic speed–power relation, rendering a tractable mixed-integer linear programming problem. The problem is efficiently solved via a tailored Benders decomposition algorithm, utilizing a genetic-algorithm warm-start to substantially accelerate convergence. Validated on three real-world networks via 1000 Monte Carlo scenarios, the proposed model reduces mean operating costs by 20.2–23.5% and suppresses cost variance by over 60% compared to deterministic approaches. Full article

We investigate the Hyers–Ulam stability of a functional equation motivated by the semi-parallelogram law, a three-variable identity that extends the classical quadratic equation. Our first result shows that every solution of this equation splits uniquely as the sum of an additive mapping and a quadratic mapping. Under natural growth conditions on the error term, we obtain stability estimates via two classical routes: the direct method and the fixed point alternative in generalized metric spaces. What makes the three-variable setting worthwhile is that it accommodates asymmetric control functions, for instance, $\phi (x_1,x_2,x_3)=\parallel x_1-x_2{\parallel }^p{\parallel x_3\parallel }^p$, which cannot be captured by the standard two-variable quadratic equation. This extra freedom proves useful when perturbations depend on the relative position of the variables, rather than on each variable separately. We also examine power-type perturbations in detail. The analysis reveals a critical exponent $p=2$: when $p<2$, stability holds with explicit constants; at $p=2$, the contraction argument breaks down, pointing to an intrinsic limitation of the method. Several examples accompany the main results, among them a closer look at the role of the critical exponent and a visual discussion of how asymmetric controls arise in practice. Full article

Public technical disclosures support transparency, certification, procurement, interoperability, and cybersecurity governance, but AI-assisted open-source intelligence (OSINT) can make dispersed records easier to connect and reuse. This article introduces AI-Enabled Technical Exposure Risk (ATER), a disclosure-review framework for deciding how public technical detail should be reported when aggregation and linkage increase its security relevance. The framework is developed through a cutover-bounded corpus of public EUCC/Common Criteria certification artifacts available through the ENISA certificate portal by 15 April 2026. This study combines conceptual framing, descriptive corpus screening, and pilot feasibility checking of the disclosure-review workflow. The results show that public certification artifacts can contain recurring traceability signals, candidate technology linkages, version or scope uncertainty, evaluation-boundary indicators, and related security-relevant disclosure patterns that become more visible when reorganized at aggregate level. A practical disclosure-risk flowchart was derived from the study codebook and pilot-calibrated against evidence-tier and disclosure-decision categories in the corpus. The analysis characterizes aggregate publication-review patterns rather than inferring product-specific vulnerabilities. The resulting model offers authors, evaluators, and governance teams a structured basis for routing technical detail toward aggregate reporting, abstraction, contextual caveating, internal evidence retention, or further legal, certification, or security review. Full article

The increasing integration of power electronic converters in hybrid AC/DC microgrids introduces significant challenges in maintaining DC-link voltage stability during mode transitions, where uncoordinated actions cause large voltage deviations. This paper proposes a coordinated hierarchical control strategy incorporating a supervisory finite state machine (FSM) and a slew-rate-limited reference shaping mechanism to ensure smooth transitions in a microgrid interfaced through a bidirectional DC–DC converter and a three-level T-type inverter. The supervisory layer coordinates the sequencing of subsystem activation and routes all mode changes through a dedicated transition state in which the power reference is gradually shaped to suppress DC-link disturbances, while a dedicated resynchronization state manages reconnection to the grid after a sustained outage. The strategy is validated through detailed switching-level simulations across five operating scenarios, including islanded load energization, grid blackout, discharging-to-charging transitions, state-of-charge limit management, and grid restoration through reclosing and resynchronization, and is further compared against a droop-based coordination scheme. Simulation results demonstrate that the proposed approach reduces the transient DC-link voltage deviation from approximately 18–20% to below 7%, and to as low as 2.6%, without introducing steady-state error, confirming its effectiveness in enhancing the dynamic stability of the system during mode transitions. Full article

Electrochemical conversion of carbon dioxide (CO₂) to ethanol offers a potential route for integrating carbon utilization with low-carbon electricity; however, its environmental performance is governed by the complete process system rather than by catalytic selectivity alone. This study presents a detailed attributional cradle-to-gate life cycle assessment of anion-exchange-membrane (AEM) and bipolar-membrane (BPM) electrolyzer systems using a functional unit of 1 kg of ethanol at the plant gate. The foreground inventory combines stoichiometric balances, peer-reviewed electrochemical evidence, process-energy estimates, and transparent engineering assumptions, while background processes are represented using ecoinvent 3.7.1. Climate-change impacts are evaluated with the IPCC 2021 100-year global warming potential method. The modeled AEM and BPM systems require 23.32 and 27.92 kWh of electricity per kilogram of ethanol, respectively. Wind-powered operation yields the lowest reported impacts, at 0.318 kg CO₂-eq kg⁻¹ ethanol for AEM and 0.442 kg CO₂-eq kg⁻¹ for BPM. Photovoltaic scenarios yield 1.812 and 2.231 kg CO₂-eq kg⁻¹, whereas the Austrian-grid scenarios yield 1.349 and 4.686 kg CO₂-eq kg⁻¹, respectively. Electricity supply is the dominant environmental driver, while separation heat, carbon utilization, component lifetime, and oxygen co-product treatment remain important secondary parameters. The BPM Austrian-grid result is disproportionately high relative to the 19.7% increase in modeled electricity demand and therefore requires exchange-level verification before it can be interpreted as a physical membrane effect. Overall, environmentally credible CO₂-to-ethanol deployment requires low-carbon electricity, reduced cell voltage, efficient carbon management, concentrated product streams, durable components, and transparent co-product accounting. Full article