Search Results (4,332)

Recent studies have witnessed significant advances in unified multi-weather image restoration, which aims to handle diverse weather degradations within a single model. In this work, we observe that rain, haze, and snow restoration exhibit substantial differences in both degradation characteristics and learning dynamics, making straightforward joint optimization prone to performance imbalance and ineffective knowledge transfer. To this end, we propose UMWIR-Net, a unified multi-weather image restoration network equipped with an Asymmetric Task Collaborative Learning strategy. ATCL consists of Intra-Task Difficulty Optimization and Inter-Task Contribution Scheduling. Specifically, Intra-Task Difficulty Optimization jointly models the remaining restoration error and recent learning progress to dynamically estimate the optimization difficulty of each weather task, thereby assigning larger weights to slowly converging and under-optimized tasks. Inter-Task Contribution Scheduling measures the directional influence of a source-task update on the validation objective of a target task, constructs an asymmetric task-contribution matrix, and accordingly promotes tasks that provide stronger transferable knowledge while compensating those that benefit less from collaborative learning. In this manner, different weather restoration tasks collaborate selectively and asymmetrically, allowing the model to exploit complementary knowledge across tasks and improve overall restoration performance. Furthermore, UMWIR-Net adopts a wavelet-based Transformer backbone to capture low- and high-frequency information, enabling effective modeling of both global structures and local details for diverse weather restoration. Extensive experiments on multi-weather image restoration datasets show that UMWIR-Net achieves state-of-the-art performance and delivers more balanced restoration quality across rain, haze, and snow removal. Full article

Global biodiversity policy increasingly depends on community-led conservation, yet the comparative evidence base contains little from South Asia’s frontier regions. This article asks how a long-running grassroots organisation in a politically and ecologically marginal region combined community mobilisation, vernacular knowledge, scientific evidence, and engagement with public institutions in pursuing conservation outcomes, and which features of that process may be relevant beyond Northeast India. Four campaigns of Nature’s Beckon, founded in Dhubri, Assam, in 1982, are compared as distinct types of intervention: species-led protected-area mobilisation at Chakrashila; landscape-scale conservation against extractive pressure at Dihing Patkai; species research with public ecological education; and community-managed institution-building. The available evidence indicates a documented and substantial, though not exclusive, role in campaigns associated with the notification of two protected areas whose current notified areas total approximately 279.83 km². Advocacy alone does not adequately explain these outcomes: where a formal government decision was required, sustained organisational capacity became consequential only when it coincided with a favourable political and administrative opening. Measured against four design features associated with successful community-based conservation, the model corresponds strongly to capacity-building investment and external linkage, in qualified form to equitable benefit-sharing, and only partly to tenure security. The article develops an ecology of the margins framework and specifies which elements appear transferable and which do not. Full article

The recovery and reuse of construction materials following disasters has emerged as an important strategy for reducing construction waste, improving resource efficiency, and enhancing the resilience of post-disaster housing reconstruction. However, existing studies remain fragmented across different disaster contexts and material categories, limiting a comprehensive understanding of the engineering, environmental, and institutional factors influencing material recovery decisions. This systematic literature review synthesises current evidence on the recovery and reuse of construction materials in post-disaster housing reconstruction. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of the Scopus and EBSCO databases identified 303 records published between 2015 and 2026, of which 69 studies satisfied the inclusion criteria. Of these, 20 studies directly examined post-disaster contexts, while the remaining 49 addressed broader construction material recovery and reuse and were included as transferable engineering evidence relevant to post-disaster reconstruction. The review synthesised evidence from both disaster-specific studies and broader research on construction material reuse to provide a comprehensive understanding of material recovery practices applicable to post-disaster housing reconstruction. The review demonstrates that both structural and non-structural construction materials have varying potential for recovery and reuse following disasters. The findings further indicate that reuse suitability is governed not only by material type but also by the interactions among disaster characteristics, residual material condition, structural integrity, contamination, durability, regulatory compliance, and intended reuse applications. Recovery pathways were found to depend on condition-based engineering assessment, while successful implementation is further influenced by economic feasibility, institutional capacity, stakeholder coordination, and recovery infrastructure. This review advances existing knowledge by synthesising disaster characteristics, engineering assessment requirements, recovery pathways, and implementation considerations into an evidence-derived condition-based perspective for construction material recovery. The proposed conceptual framework provides an evidence-informed reference for engineers, emergency management agencies, policymakers, local authorities, and construction practitioners seeking to integrate reusable construction materials into resilient and resource-efficient post-disaster housing reconstruction. Full article

Hyperspectral image super-resolution (HSI SR) focuses on enhancing the spatial resolution of HSIs while preserving their inherent spectral information. Existing single-image HSI SR methods still suffer from blurred spatial edges and spectral distortion. Although numerous spatial–spectral enhancement networks can enhance spatial–spectral feature extraction, they often lead to a cumbersome network architecture. To address these issues, we propose a Spatial–Spectral Distillation Mamba Network, called SSDM-Net, for HSI SR, which contains a main reconstruction branch and two training-only auxiliary branches for spatial and spectral knowledge distillation. Specifically, the spatial and spectral auxiliary branches, which are utilized exclusively during training, provide edge-aware guidance and capture spectral correlations, respectively. During training, the spatial–spectral knowledge is transferred to the main branch. During inference, the auxiliary branches are removed, improving reconstruction quality without extra computational burden. In the main branch, a Mamba-based spatial–spectral global enhancement module processes spatial and latent inter-channel sequences using selective scanning whose cost is linear in the processed sequence lengths when the feature dimensions are fixed. In addition, a dynamic loss weighting strategy is developed to balance reconstruction, distillation, and auxiliary losses during optimization. Comprehensive experiments conducted on the CAVE and Houston datasets with three scale factors demonstrate that SSDM-Net produces more accurate reconstruction results than existing representative HSI SR methods. Cross-dataset experiments on the Harvard dataset further suggest that the method can maintain competitive reconstruction performance under the evaluated cross-dataset settings. Full article

Architecture design studios are complex learning environments in which knowledge develops through critique, collaboration, individual reflection, material exploration, and increasingly digital forms of practice. Although the physical organization of these spaces influences how such activities unfold, research on furniture arrangement remains fragmented across ergonomics, learning environments, educational research, technology-enhanced education, and participatory design. This structured integrative review examined the mechanisms reported between furniture arrangement, related physical spatial configurations, and learning processes in architecture design studios and relevant adjacent educational settings. It also investigated the pedagogical, ergonomic, technological, and cultural conditions under which these mechanisms were reported as supportive or restrictive. Scopus, Web of Science All Databases, and ScienceDirect were searched on 7 August 2026. The searches identified 64 source-level records: 26 from Scopus, 19 from Web of Science, and 19 from ScienceDirect. Eight duplicate records were identified within the ScienceDirect results. Following cross-source deduplication, title and abstract screening, and full-text eligibility assessment, 15 publications were included in the integrative synthesis. Study characteristics, methodological quality, contextual relevance, furniture-related conditions, pedagogical activities, reported outcomes, explanatory mechanisms, evidentiary directness, and transferability were examined through an integrative, mechanism-oriented synthesis. The resulting relationships were subsequently organized through an abductive framework-development process into the provisional Adaptive Studio Learning Ecosystem (ASLE) framework. ASLE comprises five interrelated dimensions: spatial flexibility, ergonomic responsiveness, pedagogical mediation, technological support, and cultural–participatory fit. The synthesis supports activity–layout alignment rather than a universally optimal furniture configuration and indicates that spatial adaptability becomes educationally meaningful only when supported by appropriate pedagogical practices, ergonomic conditions, technological integration, and patterns of user participation. Because the evidence base includes a limited number of direct architecture-studio studies and relies partly on mechanisms transferred from adjacent settings, ASLE should be treated as a provisional, review-derived framework requiring empirical validation. Full article

Engineering education research (EER) draws on a wide range of philosophical traditions, yet the ontological, epistemological, and axiological (OEA) commitments that shape how knowledge is produced are rarely made explicit in published work. This study investigated the prevalence and nature of OEA positioning across a purposive sample of 54 papers published in 2024 in three Q1 engineering education journals: the Journal of Engineering Education, the European Journal of Engineering Education, and the Australasian Journal of Engineering Education. Using critical realism as a metatheoretical framework, we developed an OEA coding instrument and applied it through an AI-assisted abductive coding process, assigning ontological, epistemological, and two-level axiological codes to each paper and assessing their internal coherence. The overwhelming majority of papers carry implicit rather than declared OEA commitments, a pattern consistent across journals and methodologies. The field is genuinely philosophically plural, with ontological positions ranging from naïve realism to social constructionism and critical realism, but the lack of clarity in this space potentially has consequences for knowledge transfer to practice, cumulative knowledge-building, and the coherence of individual studies. Full article

Immersive visualization can support interpretation of architectural heritage in historical paintings, yet translating 2D pictorial evidence into navigable 3D scenes remains challenging. Conventional workflows rely on physical survey data, while direct generative AI (GenAI) may produce structural hallucinations and lack historical constraints. This study proposes a human-in-the-loop GenAI-assisted framework for producing immersive 3D visualization prototypes rather than historically verified reconstructions. It integrates multi-view image generation, knowledge-informed review, single-image-to-3D generation, topology inspection, and perceptual calibration. Four fragments from the Northern Song Dynasty painting Along the River During the Qingming Festival were examined as a single-case proof of concept. Across three tested model pairs, raw AI assets were generated in approximately 3–4 min and were suitable for distant-background use; close-up visualization required 1–2 h of refinement, while basic structural editability required 4–5 h of post-processing, reducing the initial time advantage. A mixed-methods study with nine domain experts and 30 non-expert participants used the UES-SF, an adapted VisAWI, and semi-structured interviews analyzed through inductive thematic analysis. All eight subscale scores exceeded their neutral midpoints after Bonferroni correction (all adjusted $p<0.001$), indicating favorable perceptions of the guided experience. Interviews suggested potential for spatial exploration, museum interpretation, and education. However, geometric discontinuities, detail loss, color deviation, and historical-semantic errors remained, requiring expert review and manual correction. Transferability beyond this artwork and architectural tradition remains untested. Full article

Nanomaterials are increasingly used in manufacturing, medicine, consumer products, and environmental technologies due to their unique physicochemical properties. Although these materials offer substantial technological and societal benefits, their widespread use has raised concerns about potential health risks. Of particular importance is exposure during pregnancy, as certain nanoparticles can cross the placental barrier and reach the developing embryo. Fetal tissues are highly sensitive to environmental insults, so maternal exposure to nanoparticles may disrupt normal development and increase the risk of abnormal pregnancy outcomes. This review examines the current understanding of nanoparticle-induced developmental toxicity, with a focus on the vulnerability of the maternal–fetal unit. We discuss the structure and function of the placental barrier and the mechanisms that enable nanoparticle transfer from mother to fetus. Particular attention is given to how nanoparticle characteristics, including size, shape, composition, and surface chemistry, influence biodistribution, placental transport, tissue accumulation, and toxicity. We summarize the major molecular and cellular mechanisms implicated in fetotoxicity, highlighting oxidative stress, apoptosis, autophagy, and DNA damage as recurring pathways identified across experimental studies. These interconnected processes contribute to placental dysfunction, impaired fetal growth, developmental abnormalities, and adverse pregnancy outcomes. We also compare findings across different classes of nanoparticles, including metal, metal oxide, carbon-based, and polymeric nanomaterials, identifying both shared toxicological mechanisms and material-specific effects. Evidence from animal models demonstrates that susceptibility varies according to nanoparticle properties, exposure conditions, and species, underscoring the complexity of nanoparticle–biological interactions and the limitations of extrapolating experimental findings directly to humans. Overall, the available evidence indicates that nanoparticle exposure during pregnancy represents a potential risk to fetal health, although important knowledge gaps remain regarding human exposure and long-term developmental outcomes. A better understanding of the mechanisms underlying nanoparticle-induced fetotoxicity is essential for improving human health risk assessment, refining experimental models, informing regulatory policies, and supporting the safe-by-design development of nanomaterials. Such knowledge will help ensure the responsible application of nanotechnology while minimizing potential risks during pregnancy. Finally, this review is distinguished by its integrated analysis of how the chemical characteristics of nanoparticles govern placental transfer and the mechanistic pathways of fetotoxicity across multiple nanomaterial classes, providing a unified framework that connects material properties with their potential for abnormal fetal development and adverse pregnancy outcomes. Full article

Plastic contamination (plastamination) has become a pervasive environmental threat with growing implications for human health. Among plastic-derived contaminants, micro- and nano-plastics (MNPs) are of particular concern due to their persistence, widespread distribution, and capacity to interact with biological systems. Humans are exposed to MNPs through ingestion, inhalation, dermal contact, and maternal transfer, and these particles can cross biological barriers, including the blood–brain barrier, reaching the central nervous system. MNPs disrupt cellular homeostasis by inducing oxidative stress, mitochondrial dysfunction, and inflammation. In the brain, these processes drive glial activation and chronic neuroinflammation, which are closely associated with neuronal damage and neurological disorders, including Parkinson’s disease (PD). MNPs can also affect systemic pathways such as the gut–brain axis (GBA) and neuroendocrine regulation, suggesting broader physiological consequences. This narrative review synthesizes current evidence on the neurotoxic and pro-inflammatory potential of MNPs. Since MNPs may promote the aggregation of proteins implicated in neurodegeneration, such as alpha-synuclein, their possible role in PD is discussed. Despite several knowledge gaps, MNPs may be emerging environmental risk factors for brain health and neurodegenerative diseases such as PD. Nevertheless, there is a need for further studies in the field, standardized methodologies and longitudinal studies to implement effective mitigation strategies. Full article

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman $\rho =-0.25$ with prediction error, $p=0.006$) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at $F_1=0.70$ with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to $F_1=0.78$ with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator $\kappa =0.74$ on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

The gut microbiota is a metabolically active ecosystem that influences host physiology through bioactive metabolites and interactions with host signaling pathways. Recent research has established a bidirectional gut–muscle axis in which microbial metabolites and muscle-derived factors (myokines) regulate muscle protein synthesis, degradation, regeneration, fiber-type specification, and overall muscle performance. Studies using germ-free, antibiotic-treated, probiotic-supplemented, and fecal microbiota transplantation models demonstrate that the gut microbiota is a critical determinant of skeletal muscle mass and function. Key mediators include short-chain fatty acids, bile acids, aromatic amino acid metabolites, microbial-associated molecular patterns, and methylamine metabolites. This review summarizes current mechanistic knowledge of gut–muscle communication and its relevance to livestock production. In monogastric livestock, particularly pigs and poultry, microbiota transplantation experiments and targeted probiotic interventions provide causal evidence that gut microbial communities influence muscle growth, muscle fiber composition, intramuscular fat deposition, carcass traits, and meat quality, including tenderness, marbling, water-holding capacity, and flavor. Several studies have also identified specific microbial taxa and metabolites capable of transferring desirable production phenotypes. In contrast, evidence in ruminants remains largely associative and originates mainly from multi-omics and dietary intervention studies. Future research should validate causal mechanisms, identify robust microbial biomarkers, and develop species-specific microbiome-based strategies for precision livestock production. Full article

Boundary-layer atmospheric stability, wind-shear variability, and thermodynamic forcing are all important factors for the initiation, intensification, and transport of dust storms. But there is limited knowledge of the quantitative evaluation of bulk-layer atmospheric stability and the relation between wind-driven dust dynamics and atmospheric stability in arid and semi-arid regions. In this research, a hybrid AI–meteorology framework, HyMet-Fusion, is presented that combines visual information derived from satellite observations with physics-based indicators of atmospheric stability to evaluate atmospheric stability during dust storm events over Iraq. The proposed framework is based on the use of deep features extracted from the satellite imagery through a frozen EfficientNetB0 backbone, combined with indicators derived from the ERA5 pressure level data for the atmosphere, such as the Bulk Richardson Number (Bulk Ri), the Wind Shear (WS) and the Dry Air Index (DAI). The two branches were merged using a late fusion (0.75 physics/0.25 image) and each hour was classified into three atmospheric stability conditions: Relatively Stable, Moderately Unstable and Unstable. The overall hourly accuracy using a Leave-One-Event-Out (LOEO) cross-validation scheme, where each dust event was used for independent testing and no dust event was used for training, was 72.4%, with 81.2% accuracy for the dominant stability state and 92.2% correct assessment of the unstable condition time for the severe dust events. Inaccuracies were mainly (66%) in the conservative direction (more instability). Unstable atmospheric conditions were also found to be associated with all severe dust storms and coincided with higher wind shear, lower Bulk Ri values and higher thermodynamic variability. Moderate and light dust events were primarily associated with transitional and relatively stable atmospheric conditions, and differed between the various regions, primarily in Kirkuk and Nasiriyah. Correlation analysis showed that wind shear had the highest correlation with atmospheric instability (r = 0.92), followed by DAI (r = 0.90) and Bulk Ri (r = −0.75). In addition, the wind shear also increased significantly from light to severe dust events at all stations investigated, showing that wind shear is a critical factor for turbulent mixing, vertical momentum exchange and dust uplift processes. The results suggest wind shear is the leading dynamics mechanism for bulk-layer instability in Iraqi dust storms. The findings highlight the complementary benefit of using physics-based atmospheric indicators embedded with deep learning satellite image analysis. The HyMet-Fusion system can be used as a transferable method for observing wind-driven instability of the atmosphere and related dust hazards, which could be employed in boundary-layer meteorology, air-quality forecasting, aviation safety and environmental risk assessment in arid and semi-arid areas. Full article

While low-impact development and retention strategies are widely studied in urban and agricultural contexts, a distinct knowledge gap remains regarding the quantitative evaluation of dispersed, natural microretention structures in small, ungauged, mountainous forested catchments under complex topographic conditions. To address this limitation, this study provides a novel quantitative assessment of how natural bioretention interventions—specifically arcuate deadwood log barriers, cascading reservoir systems, and strategic afforestation—influence runoff reduction and substrate infiltration dynamics. Focusing on the 4.57 km² basin of the Stankowice Stream in southwestern Poland, the research integrates field geodetic and hydrological measurements with Iszkowski’s empirical flow formulas and high-resolution digital elevation modeling (SCALGO platform). Delineation of 10 key subcatchments revealed that surface runoff potential is heavily concentrated within specific flow pathways rather than determined solely by subbasin area. In unit No. 9, deploying an arcuate arrangement of 19 deadwood logs achieved an 11% reduction in surface runoff (retaining 8662.50 m³), whereas coupling these log structures with a downstream cascading two-dam system significantly enhanced retention performance by establishing 79,065.68 m³ of depression storage and driving 264,066.16 m³ of subsurface infiltration. Furthermore, multi-scenario land use modeling demonstrated that transforming land cover to forest reduced surface runoff by over 70% in topographically steep subcatchments (e.g., unit No. 7). These findings demonstrate that effective flood mitigation in headwater catchments requires a systemic, targeted hybrid strategy combining decentralized bioretention with localized storage nodes, offering a transferable framework for sustainable regional water governance. Full article

The DC–DC boost converter is a challenging control target because of its nonlinear dynamics, wide operating range, and non-minimum-phase behavior under continuous conduction mode. These control challenges are particularly pronounced under large-signal transients, parameter variations, constant-power-load effects, and hardware constraints. This review examines deep reinforcement learning-based control of DC–DC boost converters from an engineering-oriented perspective. It covers learning-assisted classical control, direct duty-cycle control, and hybrid architectures, with attention to action design, reward formulation, observation timing, safety constraints, and validation fidelity. A structured search of Scopus, Web of Science Core Collection, and IEEE Xplore was used to identify boost-specific studies and transferable adjacent-converter evidence. Rather than ranking algorithms alone, the review organizes the literature around converter-aware and hardware-oriented learning control. The review argues that recent progress should not be interpreted as a simple replacement of classical control by deep reinforcement learning. Accordingly, algorithm choice, physical knowledge, action and reward design, observation timing, safety constraints, and validation fidelity are treated jointly. The available evidence suggests that progress toward credible practical deployment requires integrating converter physics, bounded or hybrid control authority, explicit safety constraints, and hardware-oriented validation. Full article

Class-incremental three-dimensional point cloud semantic segmentation requires models to learn newly introduced categories while preserving previously acquired knowledge without storing historical point clouds. This setting is challenged by representation drift during incremental optimization and semantic background shift caused by incomplete annotations of previously learned categories. To address these problems, this study proposes confidence-gated relational distillation, an exemplar-free teacher–student framework that combines feature-level relation preservation with semantic-level background correction. The relational component transfers normalized neighborhood-affinity distributions and weights each point according to teacher reliability, thereby reducing the influence of uncertain predictions. The background-compensation component reconstructs reliable old-class targets using class-specific thresholds and calibrates competition between previously learned and newly introduced classes. Experiments on the Stanford Large-Scale Three-Dimensional Indoor Spaces dataset and ScanNet show competitive performance across multiple incremental settings, with more consistent improvements on ScanNet. Under the ScanNet 10-1 protocol, the proposed method achieves an average mean intersection over union of 44.6% across eleven learning states and 33.3% at the final state. Under the same PointNet++ configuration, it also reduces training time and peak graphics processing unit memory. These results indicate that reliable relational transfer and adaptive background correction provide an effective balance between old-class retention and novel-class acquisition without introducing replay data or separate architectural branches during incremental optimization. Full article