Search Results (213)

Driven by the large-scale deployment of renewable electricity, water electrolysis has emerged as a leading pathway for high-efficiency hydrogen production. Under practical operating conditions, gas evolution and gas–liquid two-phase flow inside electrolyzers substantially reshape electrode interfacial states and the in-cell mass transfer environment and have been reported to cause performance losses on the order of 10–30% under unfavorable conditions. This review summarizes the evolution of electrode-generated bubbles during nucleation, growth, detachment, and coalescence, and consolidates the fundamental features of two-phase hydrodynamics and phase-distribution patterns in electrolyzer channels. Progress and limitations of major two-phase modeling approaches are then assessed with respect to their capability to resolve the relevant interfacial and transport processes. The impacts of gas evolution and two-phase flow on electrochemical performance, stability, and durability are subsequently discussed. Finally, recent advances in two-phase-flow management—through flow-field organization and structural design, as well as the introduction of external physical fields—are reviewed, together with experimental and diagnostic methods used to quantify bubble behavior and phase distributions. This review aims to provide a coherent understanding of the governing behaviors, research tools, and performance implications of gas evolution and two-phase flow in water electrolysis, and to inform electrode/transport-layer design, flow-field management, and the development of predictive numerical models. Full article

The origin of life is commonly discussed within two competing conceptual frameworks: the metabolism-first and information-first hypotheses. While each emphasizes a different defining property of early life, modern biochemistry reveals a fundamental interdependence between metabolic processes and genetic information transfer, leading to a persistent chicken-and-egg problem. In this study, we investigate a prebiotically plausible reaction system that enables the concurrent formation of molecular precursors associated with both frameworks. Under simulated Hadean hydrothermal conditions, acetylene, ammonia, cyanide, and carbon monoxide were reacted in aqueous solution in the presence of transition metal sulfides. Using gas chromatography–mass spectrometry combined with stable isotope labeling, we demonstrate the simultaneous formation of the nucleobase uracil and the amino acids alanine and aspartic acid. Isotopic incorporation patterns allow reconstruction of the underlying reaction pathways and confirm the contribution of all starting materials to product formation. While amino acids are produced continuously over the observed period in significantly higher yields than uracil, uracil formation exhibits a pronounced time-dependent maximum after three days. Variations in pH, reaction time, and metal sulfide catalysts modulate product yields but do not prevent the parallel emergence of both molecular classes. These findings support a scenario in which proto-metabolic chemistry and molecular precursors of genetic information could have arisen simultaneously within a shared geochemical setting. The results provide experimental support for a coupled origin of metabolism and transcriptional building blocks, offering a potential resolution to the dichotomy between metabolism-first and information-first models of early life. Full article

Cross-topic stance detection degrades when encoders entangle stance signals with topic-specific vocabulary, causing representations that fail to transfer to unseen targets. Existing methods commit to either topic-invariant or topic-aware representations and apply the same strategy uniformly to every input, sacrificing complementary information. We propose ReDyGait, a three-stage framework that disentangles these two types of signals through dedicated contrastive pre-training and recombines them adaptively at inference time. Stage 1 trains a topic-invariant encoder with supervised contrastive loss over cross-topic positives. Stage 2 trains a topic-contextual encoder with bidirectional pair contrastive loss over within-topic positives; both stages employ topic-aware hard negative mining to prevent shortcut learning. Stage 3 freezes the two contrastive encoders and learns a gating network that produces per-instance weights over invariant, contextual, and base-encoder pathways. On VAST, ReDyGait achieves a macro-averaged F1 of 0.782 in the zero-shot setting and 0.752 in the few-shot setting, improving over the strongest baseline by 1.1 points in both; on SEM16t6 in a leave-one-target-out setup, ReDyGait reaches an average F1 of 0.612. Analysis of the learned gate weights shows that the model shifts toward the invariant pathway for unfamiliar topics and toward the contextual pathway when topic-specific patterns are available, confirming that the disentanglement operates as intended. Full article

As a crucial pathway for optimizing land factor allocation, farmland transfer plays a pivotal role in implementing the “storing grain in land and technology” strategy and safeguarding national grain security. Based on panel data from 30 provinces in China spanning 2009 to 2023, this study employs a two-way fixed effects model and a Spatial Durbin Model (SDM) to systematically examine the mechanisms, heterogeneity, and spatial spillover effects of farmland transfer on grain production capacity. The results indicate that: (1) Farmland transfer significantly enhances grain production capacity, and this conclusion remains robust after multiple robustness and endogeneity tests. (2) Farmland transfer boosts grain production capacity by promoting cultivated land connectivity and facilitating the substitution of machinery for labor; however, the accompanying non-grain tendency and land governance disputes exert inhibitory effects on capacity release. (3) Transfers to farming households and professional cooperatives, as well as the adoption of leasing and informal exchange arrangements, exhibit the strongest positive effects on production capacity, and the scale-efficiency gains of farmland transfer are particularly pronounced in major grain-consuming areas. (4) Improvements in a region’s farmland transfer level drive the enhancement of grain production capacity in neighboring regions through the diffusion of management experience and the sharing of social services. This study provides empirical evidence and policy insights to optimize farmland transfer mechanisms and safeguard food security. Full article

Polyunsaturated fatty acids (PUFAs), especially the long-chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are essential nutrients for aquatic organisms and play key roles in growth, reproduction, neural development, and immune function. In freshwater ecosystems and aquaculture systems, the availability of these lipids depends on complex interactions within aquatic food webs, where PUFAs are produced by primary producers and transferred to higher trophic levels. This review summarizes current knowledge on the biosynthesis, regulation, and trophic transfer of PUFAs in freshwater aquaculture food webs, with particular emphasis on interactions among microalgae, zooplankton, and fish larvae. The main biochemical pathways and regulatory mechanisms responsible for PUFA synthesis in microalgae are described, together with the environmental factors that influence their production. The role of zooplankton at an intermediate trophic level is discussed, highlighting their ability to retain, modify, and transfer dietary fatty acids to higher consumers. Finally, the capacity of freshwater fish larvae to synthesize and regulate long-chain PUFAs through key metabolic enzymes is examined, along with the influence of diet and environmental conditions on these processes. By integrating information from molecular, biochemical, physiological, and ecological studies, this review provides an overview of the mechanisms underlying PUFA production and trophic transfer in freshwater aquaculture food webs. Full article

Deep decarbonization strategies at the local level have been extensively documented for cities in the Global North, yet little is known about how cities in Sub-Saharan Africa (SSA) pursue climate mitigation amid infrastructure constraints, limited fiscal autonomy, and pressing developmental needs. Local governments worldwide are recognized as critical actors in addressing urban greenhouse gas (GHG) emissions. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. This study examines how deep decarbonization pathways in four leading SSA cities (Accra, Addis Ababa, Lagos, and Nairobi) compare with those in the Global North. Using qualitative methods including document analysis and semi-structured interviews, we examine the technical pathways, institutional strategies, governance mechanisms, and actors involved in these cities’ climate mitigation efforts. Our findings reveal that while SSA cities pursue similar technical priorities to Global North cities (renewable energy, building efficiency, sustainable transport), their approaches diverge significantly in implementation. SSA cities innovate through decentralized waste-to-energy systems adapted to informal contexts, rely heavily on donor funding rather than municipal bonds, and uniquely leverage traditional institutions for community engagement. Governance structures are predominantly top-down and centralized, contrasting with the polycentric, multi-level governance observed in the Global North. These findings demonstrate that deep decarbonization in SSA must be reconceptualized not only as a form of climate mitigation but as an integrated strategy that addresses infrastructure gaps and building institutional capacity. This research contributes new knowledge on urban climate governance in developing regions and offers transferable lessons for cities facing similar constraints. Full article

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing. Full article

Fluid mechanics in disordered structures gives rise to rich multiscale dynamics through the interplay of topology, symmetry breaking, and fluid–structure interactions. Heterogeneous networks encode mechanical responses, regulate flow organization, and shape energy dissipation, enabling memory effects and emergent collective behaviors across both natural and engineered systems. These principles operate across vast scales: from seamounts with characteristic scales of $L\approx {10}^3\mathrm{m}$ and Froude numbers $Fr\approx {10}^{-2}–{10}^{-1}$ generating deep-ocean turbulent mixing, to marine tidal turbines operating at Reynolds numbers $Re\approx {10}^7–{10}^8$ and Euler numbers $Eu\approx {10}^{-1}–{10}^0$, where inertial forces dominate flow dynamics. Although the dominant physical forces may vary across scales—for example, planetary rotation and stratification in large-scale oceanic flows versus viscous or interfacial effects in microscale systems—the comparison of dimensionless parameters provides a useful framework for discussing similarities in flow organization and scaling behavior. Empirical observations, network-based descriptions, and multiscale simulations collectively demonstrate how topological features constrain symmetry, organize transport pathways, and support predictive reconstruction and inverse design. These principles underpin applications ranging from engineered systems that exploit broken symmetries to rectify chaotic transport, to biological architectures where flows mediate information transfer, locomotion, and structural self-organization. In this Review, we synthesize recent advances to propose a unifying physical paradigm: fluid flows actively interact with disorder, reorganize dissipation, and convert structural asymmetry into functional mechanical performance across scales. Full article

Biomimetic strategies have increasingly informed adaptive environmental systems; however, biomimetic mechanism transfer into interior environmental comfort remains unevenly operationalized and weakly evidence-stratified. Despite rapid post-2020 expansion of nature-inspired strategies, cross-domain translation across thermal comfort, indoor air quality (IAQ), visual comfort, and acoustic performance remains fragmented. This study addresses this gap by systematically mapping biomimetic mechanism transfer pathways within interior environmental systems, using biophilic strategies as a comparative baseline. A systematic mapping review was conducted following PRISMA 2020 guidelines to examine biomimetic mechanism transfer across interior environmental comfort domains. Studies were coded according to comfort domain, intervention scale, evidence type, and empirical strength. Results indicate three recurrent imbalances in the screened corpus: biophilic strategies dominate the literature (71.8%), intervention activity is concentrated at system scale and within multi-domain configurations, and acoustic bio-inspired optimisation is absent as a primary research domain. At the same time, the evidence base remains weakly stratified: only 10.3% of studies report statistically validated empirical findings, whereas 50.0% remain review-based or concept-led. To address these imbalances, the study proposes the Biomimetic Mechanism Transfer Mapping Framework (CPMF), a six-layer model linking biological logic, physical process activation, measurable IEQ outputs, empirical robustness, and implementation feasibility. The framework advances biomimetics by structuring mechanism translation into operational interior environmental performance systems. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

The expansion of tropical specialty livestock farming raises urgent concerns about airborne pathogen and antibiotic resistance dissemination. Ostrich farming, characterized by high-density stocking and feed exposure, yet their microbial ecology remain poorly characterized. This study analyzed 48 bioaerosols samples from an ostrich farm in Hainan, China, across dry and rainy seasons using 16S rRNA sequencing and metagenomics. The bacterial community were dominated by Firmicutes, Proteobacteria, and Actinobacteria, followed by Staphylococcus, Bacillus, and Acinetobacter as predominant genera, with particle size significantly shaping their structure. Large particles (>7.0 μm) carried higher species richness, while medium particles (2.1–3.3 μm) exhibited the highest diversity and evenness. Notably, small particles (0.65–1.1 μm), which can penetrate deep into the lungs, were enriched with Brevibacillus and Corynebacterium. Metagenomic analysis identified 638 antibiotic resistance genes (ARGs), dominated by efflux pump-associated determinants. The detection of clinically relevant ARGs (e.g., mcr-1 and bla_TEM) reflects the genetic potential of the airborne resistome, rather than confirmed resistance phenotypes or active horizontal gene transfer. Functional analysis revealed a strong potential for organic matter degradation, driven by abundant carbohydrate-active enzymes (CAZymes) and their corresponding CAZyme genes, as well as a nitrogen cycle dominated by assimilation and reduction pathways, while genes for nitrogen fixation and nitrification were absent. Our findings demonstrate that ostrich farming enhanced airborne microbial diversity and functional potential, facilitating the ARG dissemination and nitrogen transformation. This study provides critical insights into the ecological and health risks of bioaerosols in tropical livestock farms, informing environmental monitoring and risk management strategies. Full article

In photovoltaic remote sensing image segmentation tasks, fully supervised methods can achieve high accuracy. However, the high cost of pixel-level annotation significantly limits their scalability in large-scale scenarios. To overcome this annotation bottleneck, this paper proposes a zero-shot cross-modal segmentation framework based on the visual-language pre-trained foundation model (CLIP). This approach harnesses CLIP’s cross-modal knowledge transfer capabilities to achieve precise extraction of photovoltaic targets without requiring any downstream training. This paper first introduces the Layer-wise Augmented Residual Attention (LARA) mechanism to enhance fine-grained detail representation in the feature space. Subsequently, a Cross-modal Semantic Attribution Module (CMSA) is designed to generate precise activation maps by leveraging image-text alignment gradient information. Finally, the Confidence-Aware Refinement Strategy (CARS) replaces the conventional training-based denoising process, directly producing high-quality binary segmentation masks through adaptive thresholding. Comparative experiments were conducted to evaluate the proposed method against various baselines using several public datasets with varying resolutions in Jiangsu Province including Unmanned Aerial Vehicles imagery, Beijing-2, Gaofen-2, and a self-created Sentinel-2 imagery covering multiple countries. Notably, the proposed method achieved an IoU of 70.3% on the Gaofen-2 PV03 dataset with a spatial resolution of approximately 0.3 m and 50.8% on the self-created Sentinel-2 PV_Sentinel-2 dataset with a spatial resolution of 10 m. Experimental results demonstrate that our proposed approach maintains excellent cross-domain generalisation capabilities while reducing annotation costs, thereby providing an efficient and viable technical pathway for the automated monitoring of large-scale photovoltaic facilities. Full article

Antimicrobial resistance (AMR) constitutes one of the most severe and pressing threats to global public health, food security, and environmental integrity. This review synthesizes current evidence across interconnected One Health domains—humans, animals, food, and the environment—to delineate the scope, mechanisms, and drivers of AMR transmission. Our analysis reveals three principal findings. First, the scope of AMR is alarmingly extensive, with antibiotic-resistant bacteria (ARB) and genes (ARGs) now pervasive across all four ecological compartments, transcending traditional clinical boundaries. Second, this widespread distribution is critically facilitated by horizontal gene transfer mechanisms, particularly via mobile genetic elements such as plasmids, which enable ARGs to disseminate rapidly between diverse bacterial populations across different ecosystems. Third, we identify multiple interconnected drivers that actively promote this cross-ecosystem spread, encompassing both evolutionary and transmission drivers. By characterizing these critical transmission pathways and underlying drivers, this review provides an integrated framework to identify critical transmission risks and inform integrated strategies for mitigating antimicrobial resistance across One Health domains. Full article

The mechanical behavior of metallic core–shell nanoparticles is critical for their use as reinforcement particles and additive manufacturing feedstocks, yet their deformation mechanisms remain incompletely understood. This study employs molecular dynamics simulations to investigate the compressive response of a Cu-core/Al-shell nanoparticle and compares it with solid Cu, solid Al, and a hollow Al shell of the same size under uniaxial loading along ⟨100⟩, ⟨110⟩, ⟨111⟩, and ⟨112⟩ directions. The single-material nanoparticles show strong anisotropy: solid Cu exhibits orientation-dependent transitions from dislocation slip to deformation twinning, while introducing a void to form a hollow Al shell reduces stiffness and strength, confines plasticity to the shell wall, and suppresses extended load-bearing twins. The Cu–Al core–shell nanoparticle combines these behaviors in an orientation-dependent manner. Under ⟨110⟩ and ⟨112⟩ loading, deformation is largely shell-dominated, whereas ⟨100⟩ and ⟨111⟩ loading more strongly activates the Cu core. Mechanistically, ⟨100⟩ is characterized by Shockley partial activity and junction/lock formation in the Al shell coupled with twinning in the Cu core; ⟨110⟩ shows primarily shell partials with limited core involvement; ⟨111⟩ promotes partial-dislocation activity in both shell and core; and ⟨112⟩ produces localized, twin-dominated bands in the Al shell with shell-thickness-dependent twin extension into the Cu core. These trends are rationalized using Schmid factor considerations for $\left\{111\right\}⟨110⟩$ slip and $\left\{111\right\}⟨112⟩$ partial/twinning shear, together with the effects of faceted free surfaces and the Cu–Al interface. The core–shell geometry enables two concurrent interface-mediated pathways, i.e., (i) stress transfer and reduced cross-interface transmission and (ii) circumferential bypass within the shell, which together yield only slight flow-stress increases over solid Al while markedly reducing stress serrations compared with both solid Cu and solid Al. Across all orientations, the core–shell structures also exhibit delayed yielding (higher yield strain) relative to solid Cu, indicating enhanced ductility. The results provide an atomistic basis for designing Cu–Al core–shell nanoparticles for robust particle-based processing and additive manufacturing feedstock, and for informing multiscale models with mechanism-resolved, orientation-dependent inputs. Full article

Flood inundation mapping (FIM) is essential in disaster risk management, infrastructure planning, and climate adaptation. Traditional hydrodynamic models, such as the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) and LISFLOOD-Floodplain (LISFLOOD-FP), provide physically interpretable flood simulations but are often data- and computation-intensive and difficult to scale across regions. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as data-driven alternatives that leverage remote sensing observations, digital elevation models (DEMs), and hydro-climatic datasets to enable scalable and near-real-time flood mapping. Our review synthesizes recent advances in ML-based flood inundation mapping, categorizing methods into traditional machine learning techniques (e.g., Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB)), deep learning architectures (e.g., Convolutional Neural Networks (CNNs), U-Net, Long Short-Term Memory networks (LSTM)), and emerging hybrid and physics-informed frameworks. We evaluate model performance across flood extent and flood depth estimation tasks, highlighting strengths, limitations, and common benchmarking practices reported in the literature. The review identifies key challenges related to model interpretability, data bias, transferability, and regulatory acceptance, and highlights recent progress in explainable artificial intelligence (XAI), uncertainty-aware modeling, and physics-informed learning as pathways toward operational adoption. By unifying terminology, performance metrics, and methodological comparisons, this review provides a coherent framework for advancing trustworthy, scalable, and decision-relevant flood inundation mapping under increasing climate-driven flood risk. Full article