Search Results (25,399)

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering. Full article

Building Information Modelling has become a common paradigm in the construction industry. To bridge the gap between end users and BIM data, some studies have adopted Natural Language Processing (NLP) in the BIM applications. Due to the incorrect segmentation of users’ natural language, most NLP-based BIM applications usually provide users with redundant or inaccurate BIM data. Sequence labeling has been widely studied in the area of NLP to find correct segments of a natural language sequence. However, the existing sequence labeling schemes perform poorly for specific BIM models. To address this issue, this study proposed a BIM model of an adaptive natural-language Sequence Labeling scheme using Machine learning, termed BIM-SeL. We first presented the problem definition of sequence labeling and the overall framework of the BIM-SeL. The BIM-SeL employs Conditional Random Field (CRF) to model the sequence labeling problem and Machine learning to train a sequence labeling model using a corpus of millions of data from the news and web domains. Then, a BIM dictionary extraction algorithm is developed to collect the exclusive vocabularies from the BIM models. A BIM dictionary-enhanced sequence labeling scheme is proposed to achieve the BIM model adaptive sequence labeling, by jointly utilizing the trained sequence labeling model and the BIM dictionary. To further enhance contextual representation and compare with state-of-the-art deep learning methods, we extend BIM-SeL with an advanced BERT*-BiLSTM-CRF model under the same framework. The effectiveness of the BIM-SeL was verified through two real-world projects, the BUCEA Library and a water pump house. The experiment results showed that the sequence accuracies of BIM-SeL in the BUCEA Library and the water pump house projects achieved 92.61% and 93.41%, respectively, and the vocabulary accuracies reach 96.77% and 97.32%, respectively. Compared with the original CRF-based sequence labeling algorithm, the BIM-SeL improved the sequence accuracies by 7.05 and 18.50 times, and the vocabulary accuracies by 1.33 and 2.48 times, in the two projects. Meanwhile, the BERT-BiLSTM-CRF variant obtains up to 99.93% vocabulary accuracy on real BIM test sequences, further validating the generality and advancement of the proposed framework. These observations proved that the BIM-SeL contributed to the natural language understanding of BIM applications using BIM data and could bridge the gap between users and BIM data. Full article

In nuclear power, marine engineering, and other fields, a matching system composed of duplex steel and copper alloy is a common combination for rotating components in a seawater environment. However, this system is susceptible to galvanic corrosion that seriously threatens its service safety and service life, with ZCuAl10Fe5Ni5 being the main component corroded. Additionally, current corrosion research on this system has evident gaps. Specifically, the influence of area ratio on galvanic corrosion remains insufficiently understood, and the action mechanism of Cl⁻ on the ZCuAl10Fe5Ni5-based corrosion product film in seawater, as well as the product evolution path, has not been fully revealed, which restricts the development of targeted protection technologies. This study explores the degradation mechanism of ZCuAl10Fe5Ni5 in a specific high-salinity environment (20,000 mg/L Cl⁻), characteristic of nuclear power plant service conditions. The results show that due to the significant electrode potential difference between the SAF2507 duplex steel and ZCuAl10Fe5Ni5 copper alloy, a stable galvanic couple is formed, with ZCuAl10Fe5Ni5 acting as the anode and undergoing dissolution corrosion. When the area ratio of ZCuAl10Fe5Ni5 (anode) to SAF2507 duplex steel (cathode) is 1:50, a significantly stronger galvanic effect is observed. The high concentration of Cl⁻ in seawater can damage the surface of the ZCuAl10Fe5Ni5-based corrosion product film, leading to intensified local corrosion. The ZCuAl10Fe5Ni5-derived corrosion products have a layered structure mainly comprising a mixed system of Cu-Al-Mg oxides/hydroxides, and the corrosion process is accompanied by selective aluminum depletion corrosion. This study provides insight into the corrosion mechanism and key influencing factors of ZCuAl10Fe5Ni5 in the matching system, as well as a theoretical basis and technical support for the design of compatibility metal materials in a seawater environment and the control of corrosion in ZCuAl10Fe5Ni5. Full article

Ottoman mosques represent a unique synthesis of architectural elegance and structural ingenuity, where massive masonry domes are balanced on slender supports through carefully engineered load transfer systems. These monumental buildings, constructed centuries ago without modern analytical tools, continue to challenge contemporary engineers seeking to understand their behavior under seismic loading. This study presents an integrated evaluation of the structural and geotechnical performance of the 16th-century Sultan Selim Mosque in Konya, Türkiye, one of the most prominent examples of Classical Ottoman architecture. The research combines ambient vibration testing (AVT), geotechnical investigations, and finite element modeling (FEM) to assess the existing structural condition and soil–structure interaction (SSI) effects. Dynamic identification through AVT provided the modal characteristics of the mosque, which were used to calibrate a detailed three-dimensional FEM developed in ANSYS Workbench using a macro-modeling approach. The numerical analyses showed that observed deformation patterns and stress concentrations are consistent with field damage observations, indicating that differential settlements and heterogeneous subsoil stiffness are the primary factors influencing the structural response. The findings enhance understanding of the seismic behavior of monumental masonry domed structures and offer a solid basis for the evaluation and conservation of Ottoman-era architectural heritage. Full article

In-well hydraulic fracture monitoring based on joint low-frequency distributed acoustic sensing (LF-DAS)/distributed temperature sensing (DTS) enables the acquisition of optical fiber mechanical strain data, which reflect fracture propagation and rock deformation during hydraulic fracturing. This paper presents an analytical method to interpret the mechanical strain profile measured by in-well LF-DAS/DTS during the fracturing process based on strain transfer theory and the Sneddon solution for fracture propagation. The analytical method is validated by a numerical model that simulates the strain field induced by fracture propagation. The sensitivity of the fiber strain to key factors, such as fracture geometry parameters and gauge length, is analyzed. The results indicate that compressive strain in the formation adjacent to the propagating fracture remains observable from the mechanical strain profile under the low fiber lag parameter condition. The presented method is applied to analyze the mechanical strain profile measured from a fractured horizontal well. Considering the reactivation of the pre-existing fracture, the location of the fractures is identified, and the fractures’ geometric parameters are inverted. This study provides a quantitative evaluation method for fracture geometry characterization based on joint LF-DAS/DTS fracturing monitoring. Full article

This article explores access intimacy as feeling, interactional practice, and political vision through an inclusive research project in Hong Kong, where 12 visually impaired adults and 35 university students collaboratively developed accessible board games. Drawing on Mingus’s interdependence framework and Valentine’s justice-based access, we position visually impaired participants as primary knowledge producers while critically examining vulnerability, power dynamics, and research ethics. Analysis of field observations and in-depth interviews reveals three key dimensions: (1) collaborative game design enabled visually impaired participants to experience emotional access by fostering friendship, recognition, and belonging beyond logistical accessibility; (2) negotiation around “independence” and “fairness” generated transformative empowerment for both visually impaired and sighted participants, reframing interdependence as strength; and (3) reciprocal vulnerability in sighted guiding practices disrupted ableist assumptions about autonomy, care, and risk, revealing care as mutual rather than unidirectional. We argue that access intimacy functions as a learnable relational skill, and that attending to it in research design, community planning, and accessibility policy fosters justice-based paradigms that move beyond accommodation toward genuine interdependence and solidarity. Full article

As generative artificial intelligence (GenAI) tools undergo rapid iteration, the complexity and heterogeneity of teachers’ technology practices in authentic instructional contexts warrant closer empirical scrutiny. Focusing on a public middle school designated as an AI demonstration site in eastern China, this study drew on 17 months of fieldwork that combined critical incident interviews, participant observation, and artifact collection. Systematic thematic analysis yielded four distinct practice types: Implicit Empowerment, Ritualized Enhancement, Transformative Exploration, and Prudent Distancing. The differentiation among these types was traced to the interplay of four dimensions: professional agency, technological cognition, organizational governance, and field culture. Specifically, the professional agency dimension encompasses trade-offs in labor intensity, preservation of professional authority, and continuity of pedagogical habitus; the technological cognition dimension manifests as misalignment of technological empowerment, concerns over output hallucinations, and the narrowing of dialogic value; the organizational governance dimension includes evaluation system orientation, excessive resource consolidation, and a lack of tolerance for innovation failure; and the field culture dimension involves peer practice modeling, team cultural atmosphere, and stakeholder demands. Together, these factors help explain the diversity of teachers’ technology adoption behaviors and offer an empirically grounded framework for understanding the micro-level processes of AI integration into classroom teaching. Full article

Background: Paramagnetic manganese (Mn(II)) has emerged as a promising alternative to gadolinium-based contrast agents (GBCAs) due to its favorable magnetic properties. Despite extensive research, no Mn-based agent has yet achieved clinical translation. Because free Mn(II) is toxic, macromolecular complexes incorporating stable macrocyclic DOTA chelators conjugated to polysaccharides may enhance coordination stability and improve the safety profile of Mn(II)-based contrast agents. Methods: Two chemical routes, maleimide- and ester-mediated, were evaluated for covalent coupling of DOTA-based macrocyclic ligands to the backbone of selected poly- and oligosaccharides. Subsequently, DOTA-modified carboxymethyldextran, aminodextran, and chitosan oligosaccharide were labeled with paramagnetic Mn(II) under mild conditions. ATR-FTIR confirmed the successful conjugation of DOTA chelators to the sugar backbone. The conjugates were further characterized by DLS, ICP-MS, and FPLC. In vitro relaxivity was measured at high field strength to evaluate MRI performance. In vivo contrast efficacy was first assessed using in ovo MRI in chicken embryos and subsequently evaluated by biodistribution studies in nude mice. Results: In vitro relaxivity studies demonstrated higher signal enhancement of the poly-/oligosaccharide-DOTA-Mn(II) conjugates compared with MnCl₂ and the clinical agent gadoteridol (ProHance^®). In ovo MRI showed persistent vascular enhancement up to 120 min, while in nude mice, contrast enhancement was observed in the liver, kidneys, and gallbladder 40 min post-injection. Conclusions: Mn(II)-tagged sugar-based imaging probes may offer a promising non-gadolinium alternative to GBCAs, with tunable biodistribution profiles depending on carrier molecular weight. Full article

Understanding the structure of multi-objective optimisation problems (MOPs) is essential for analysing search difficulty and supporting informed decision-making. In single-objective optimisation, fitness landscapes offer a spatial view of a problem, but extending such visualisations to MOPs is challenging due to the vector-valued nature of objectives. In this work, we introduce Pareto landscape, a fitness landscape visualisation technique for multi-objective optimisation on the basis of the Pareto dominance relation. We illustrate the main characteristics of a Pareto landscape, relate it to the classical fitness landscape, and examine its behaviour across benchmark suites, constrained problems, multimodal problems and real-world cases. We also show how it captures problem landscape structures relevant to optimisation difficulty. A comparison with gradient field heatmaps, PLOT, cost landscape, and constrained cost landscape further demonstrates that Pareto landscape offers complementary insight by highlighting structural patterns not visible with existing visualisation methods. Overall, the results indicate that the Pareto landscape provides a consistent way to observe problem structure across different classes of multi-objective optimisation problems. Full article

The field effectiveness of Trichogramma parasitoids (Hymenoptera: Trichogrammatidae) against lepidopteran pests is strongly influenced by temperature, which affects their development, survival, parasitism and reproductive performance. Understanding thermal requirements is therefore essential for optimizing mass rearing and release strategies. The present study evaluated the effects of five constant temperatures (25, 30, 33, 35, and 40 °C) on biological parameters of Trichogramma bourarachae Pintureau & Babault and two strains (S1 and S2) of T. cacoeciae Marchal reared on Ephestia kuehniella Zeller eggs. Emergence rates were higher between 25 °C and 33 °C for all tested parasitoids, decreased markedly at 35 °C for T. cacoeciae, whereas T. bourarachae emergence showed higher tolerance at 35 °C, and no emergence was recorded for all parasitoids at 40 °C. Parasitism capacity was strongly influenced by both temperature and parental thermal history. Trichogramma bourarachae exhibited its highest parasitism rate at 25 °C; however, females originating from the parental generation that developed at 30 °C maintained relatively high parasitism rates at elevated temperatures (30 to 35 °C). For T. cacoeciae S1, parental development at 30 °C enhanced offspring parasitism over a broader temperature range. Conversely, T. cacoeciae S2 achieved maximum parasitism when the parental generation developed at 25 °C, with high parasitism maintained at 25 °C, 30 °C, and 33 °C. At the species level, parasitism was highest between 25 °C and 33 °C, declined at 35 °C, and no parasitism was recorded at 40 °C due to the absence of survival. Within each species, however, strain-specific differences were observed, particularly at 35 °C, indicating variability in thermal tolerance and reproductive performance. Female longevity decreased with increasing temperature in all species and strains. However, individuals originating from parental generation that developed at 30 °C exhibited improved survival when exposed to elevated oviposition temperatures, indicating thermal acclimation. Increasing temperature induced a male-biased sex ratio in T. bourarachae, whereas T. cacoeciae maintained stable thelytokous reproduction across all treatments. These results emphasize the importance of thermal tolerance and parental thermal history for selecting suitable Trichogramma species and strains for mass rearing and field application for biological control under warming climatic conditions. Full article

Under climate change, extreme wind events are predicted to become both more common and more severe, increasing the vulnerability of plantation forests. In February 2023, ex-tropical Cyclone Gabrielle caused widespread wind damage to radiata pine (Pinus radiata D. Don) forests across the North Island of New Zealand, providing a rare opportunity to evaluate mechanistic wind-risk modelling under extreme storm conditions. This study assessed the performance of the ForestGALES model in predicting wind damage within the Rangipo genetic accelerator trial and examined the influence of stocking and cultivation on wind vulnerability. Using detailed pre-cyclone field measurements and high-resolution unmanned aerial vehicle light detection and ranging (ULS) data, ForestGALES was parameterised for the Rangipo trial and applied at both individual-tree and stand scales. Model predictions were compared with observed post-cyclone damage using balanced area under the receiver operating characteristic curve (AUC), accounting for strong class imbalance. Wind damage was observed in 16.7% of trees, of which 10.2% showed stem breakage and 6.5% overturning. Across both spatial scales, overturning was more accurately predicted than stem breakage. At the individual-tree scale, ForestGALES showed moderate predictive skill, with balanced AUC values of 0.650 ± 0.014 for overturning, 0.595 ± 0.011 for breakage, and 0.621 ± 0.008 for total damage. Model performance was stronger at the stand scale, where discrimination was highest for overturning (AUC 0.811 ± 0.122), followed by breakage (0.693 ± 0.116) and total damage (0.623 ± 0.083). Silvicultural treatments significantly influenced predicted critical wind speeds (CWS). High-stocking treatments exhibited consistently higher CWS values and therefore greater wind firmness than low-stocking treatments, while cultivation effects were smaller but significant. Simulated reductions in stocking further demonstrated increased wind vulnerability as stocking declined, highlighting thinning as a primary determinant of wind risk. These findings demonstrate that ForestGALES can reliably discriminate wind damage at operational stand scales under extreme cyclone conditions and highlight the importance of stand structure in improving plantation resilience under increasingly storm-prone climates. Full article

Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here, we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016–2024) imagery and multi-decadal Landsat observations (1992–2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000–2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments. Full article

Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) providing the best discrimination of dead grassland. In spatially grouped cross-validation, NBR achieved very high accuracy for dead versus non-dead grassland, with AUC = 0.9996, precision = 1.00, recall = 0.82, and F1-score = 0.90 for Sentinel-2, and AUC = 0.9982, precision = 1.00, recall = 0.62, and F1-score = 0.76 for Landsat 9. Retrospective mapping revealed four dieback events since 2000: two short-term episodes with rapid within-season recovery (2000, 2003) and two long-term events characterized by persistent degradation and slow regeneration (2012, late 2018–2019). The largest short-term event, in 2003, affected 42.19 ha of total dieback and 96.95 ha including partially damaged or regenerating grassland. Dieback extent was negatively associated with water balance deficit, strongest for SPEI-12 (ρ = −0.548, p = 0.002), while winter frost under shallow-soil conditions likely contributed to long-term damage in 2012. Geomorphological analysis indicated that elevation, terrain curvature, and, to a lesser extent, wind exposure are the primary controls on dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change. Full article

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article