Search Results (1,059)

Polarimetric Synthetic Aperture Radar (PolSAR) has significant advantages for land-cover classification for its all-weather, day-and-night, and multi-polarization observation capability. Traditional methods often exhibit limited classification accuracy in regions with strong noise and complex textures. Although deep learning methods can improve classification performance, they usually suffer from high model complexity, while lightweight models often show insufficient spatial consistency. To address these issues, this study proposes a PolSAR land-cover classification framework that integrates a Lightweight Dynamic Sequential Axial Network (LiteDSANet) with a polarization feature-guided Dense Conditional Random Field (PFG-DenseCRF). LiteDSANet is employed to generate the initial class probability map, and PFG-DenseCRF optimizes the classification results by introducing polarimetric features. Experiments were conducted on AIRSAR L-band and RADARSAT-2 C-band datasets from the San Francisco Bay and Flevoland regions, covering agricultural, urban, and natural land-cover scenes. The results show that the proposed method improves classification accuracy by 2.14~15.36% compared with other methods, while achieving a favorable balance between accuracy and computational efficiency. These results demonstrate the effectiveness of the proposed method for PolSAR land-cover classification in different regional environments. Full article

The safe operation of power battery systems relies on the dynamic symmetric equilibrium of electrochemical distribution and thermal management states, whereas fault occurrence is often accompanied by symmetry breaking. To achieve accurate fault diagnosis and symmetry restoration, this study proposes a symmetrical closed-loop framework (DBP-KG-FBN) that integrates digital battery passport (DBP) text mining, knowledge graph (KG), and fuzzy Bayesian network (FBN). Power battery fault diagnosis is critical to new energy vehicle (NEV) safety; however, conventional methods face two key limitations: (1) they inadequately exploit multi-source heterogeneous textual data in DBPs; and (2) they fail to handle uncertainty in fault propagation. The methodology proceeds as follows. First, a BERT-BiLSTM-CRF model extracts fault-related entities and relations from unstructured DBP text, which are structured into a Neo4j-based knowledge graph. Second, via rule-based topological mapping, the KG topology is transformed into a Bayesian network through structurally symmetric transformation between the semantic and probabilistic layers, with cyclic dependencies resolved by introducing latent variables. Third, network parameters are determined by integrating fuzzy set theory with game theory-based weighting to quantify uncertainty and subjectivity in expert evaluations, thereby achieving symmetric utilization of subjective and objective information. This enables bidirectional symmetric reasoning for forward fault prediction and backward fault traceability. Experimental results demonstrate that while maintaining symmetric stability of the diagnostic knowledge topology, the proposed DBP-KG-FBN method achieves a diagnostic accuracy of 0.92 (Top-3). This symmetrical closed-loop framework significantly outperforms fault tree analysis (FTA) and event tree analysis (ETA) in diagnostic accuracy and reasoning efficiency. It transforms unstructured DBP data into computable knowledge for intelligent battery diagnosis. Future work will expand the corpus via transfer learning and optimize adaptive weighting algorithms for expert evaluations. Full article

The analysis of dynamic causal mechanisms underlying shipboard fires and explosions is often restricted by the unstructured and fragmented nature of accident investigation reports. This study proposes a framework integrating ontology-driven information extraction with Dynamic Bayesian Networks (DBNs) to model temporal accident evolution. An ontology comprising 41 nodes was constructed through a structured expert elicitation process to formalize the domain knowledge. To process 198 bilingual accident reports, an extraction pipeline was deployed, incorporating XLM-RoBERTa, BiLSTM-CRF, and an entity-marker relation classifier. Large language model (LLM)-directed weak supervision, constrained by token-level information entropy filtering, was employed to expand the training corpus, necessitating only 2.5% manual verification. The extracted semantic dependencies were utilized to initialize a three-slice DBN (precursor, initial fire, and escalation/explosion). The network structure was jointly optimized through ontology constraints (112 forbidden and 4 mandatory edges), the Hill-Climbing algorithm, and BDeu scoring. The proposed DBN achieved an AUC of 0.759 ± 0.086 and a Brier Score of 0.192 ± 0.021 (1000 bootstrap iterations), demonstrating superior predictive performance over traditional interpretable models (Static BN, HMM, ETA) with large effect sizes (Cohen’s d > 1.0), while maintaining competitive accuracy and enhanced causal interpretability relative to XGBoost. This framework offers a scalable, data-driven methodology for dynamic probabilistic risk assessment in maritime safety. Full article

Controlled-release fertilizer (CRF) improves fertilizer-use efficiency through sustained nutrient release, but its rate-dependent effects on the growth and physiology of Paeonia delavayi seedlings remain unclear. In this study, germinated seeds of P. delavayi with radicles 3–4 cm in length were grown under container nursery conditions with four CRF application rates: (CK, 0 kg·m⁻³), treatment 1 (T1, 0.6 kg·m⁻³), treatment 2 (T2, 1.2 kg·m⁻³), and treatment 3 (T3, 2.4 kg·m⁻³). Morphological traits, root characteristics, biomass accumulation, physiological parameters, and chlorophyll fluorescence were evaluated, and Pearson correlation and fuzzy membership analyses were used to compare overall treatment performance within the tested range. CRF significantly promoted seedling height, leaf number, petiole length, and biomass accumulation, although the promoting effect did not increase continuously with fertilizer rate. By June, seedling height in T2 was 160% greater than that in CK, while aboveground biomass increased by 552% and 574% in T2 and T3, respectively. Root morphological traits were not significantly affected, suggesting that CRF primarily promoted aboveground development and biomass production. Medium and high CRF rates increased leaf superoxide dismutase (SOD) activity by 42% and 103%, respectively, and peroxidase (POD) activity by 163% and 250%, respectively. Aboveground starch content was 45% higher in T2 than in CK. In contrast, photosynthetic pigment contents and the chlorophyll a/b ratio were not significantly affected by CRF. Chlorophyll fluorescence analysis showed that Fv/Fm remained stable among CRF treatments (0.78–0.82) and was significantly higher than that in CK (0.65), whereas the actual quantum yield of PSII [Y(II)] did not differ significantly among treatments. Relative to CK, the quantum yield of non-photochemical quenching [Y(NPQ)] increased from 0.20 to 0.40 in T2, while the quantum yield of non-regulated energy dissipation in PSII [Y(NO)] decreased from 0.37 to 0.24–0.22 in T2–T3. Pearson correlation and fuzzy membership analyses ranked the treatments as T2 > T3 > T1 > CK, indicating that T2 performed most favorably within the tested range, although its advantage over T3 was small. Overall, an appropriate CRF rate promoted P. delavayi seedling growth and was associated with changes in biomass accumulation, antioxidant enzyme activity, carbon assimilate storage, and chlorophyll fluorescence parameters. Full article

Scientific and accurate agronomic knowledge is key to ensuring efficient wheat production. China’s vast agricultural land spans a wide range of longitudes and latitudes, and agronomic practices are closely tied to temporal factors such as wheat growth stages. So agronomic knowledge exhibits significant spatiotemporal variability. Constructing a spatiotemporal knowledge graph of wheat production can offer multi-dimensional data support and enabling deeper knowledge services. Wheat agronomic knowledge is often fragmented and unstructured and efficiently extracting text segments of agronomic knowledge and agronomic knowledge triples are two key challenges. Because of the high proportion and significant production service value of attribute values in agronomic knowledge, an attribute-rich agronomic knowledge graph schema was created. According to the characteristics of agronomic texts, a keyword attention mechanism (KAM) was proposed and integrated with an improved BERT model for sentence-level feature extraction to create an extraction model AgronomicCorpusExtraction for agronomic knowledge text corpora. The agronomic knowledge of wheat production is characterized by non-standard syntax, complex multi-layer structures, diverse entity expression methods, and a wide span of scope, and existing extraction methods cannot achieve satisfactory results. To address the issue, a joint extraction model AgronomicTripleExtraction was proposed to extract entities, attributes, and relations in different phrases, firstly the BERT and BiGRU were used jointly to extract the long and short distance features, and the CRF was used by global normalization joint modeling to extract attributes, then intermediate features between the same type of attributes extracted by average pooling to segment different entities. At last, a relation-aware relation feature enhancement (RAFE) method was created and a MLP was used to extract relations based on the relation matrix constructed from the knowledge graph schema. Ablation experiments were conducted to evaluate the performance for AgronomicCorpusExtraction with and without KAM and that for AgronomicTripleExtraction under four conditions, the model with BiGRU, RAFE, and entity segment, without BiGRU, without RAFF, and without entity segment. The results indicate that the use of KAM improves F1-score by 0.128 and AgronomicTripleExtraction achieves F1 of 0.897, 0.875, 0.871 for attribute, entity and relation extraction when using the three modules simultaneously, and removing any single module leads to a certain degree of performance degradation. Comparative experiments were conducted between AgronomicTripleExtraction and some related state-of-the-art models published recently. Full article

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks to regional ecological security. To address this challenge, this study develops a multi-actor governance analysis framework integrating deep learning, knowledge graphs, and complex network optimization. Stakeholder demands are extracted from multi-source data using a BERT-BiLSTM-CRF model, including policy documents, enterprise reports, and public discourse, and are then organized into a knowledge graph for water-ecological governance. A Relational Graph Attention Network (R-GAT) is subsequently used to transform the knowledge graph into a signed weighted network, enabling the measurement of conflict intensity and the identification of key conflict nodes across governance scenarios. Based on multi-objective optimization, a Pareto frontier is constructed to balance conflict tension, fairness, and governance efficiency, from which a compromise solution for responsibility weighting is identified. An empirical case study of a typical city in the Yellow River Basin shows that the proposed framework can identify core conflict nodes and provide quantitative support for conflict mitigation and coordination adjustment. The findings offer a quantitative reference for institutional innovation and evidence-based decision-making in urban water-ecological governance. Full article

Background and Objectives: The presence of comorbidities in both the pre- and post-diagnostic periods is a critical consideration in the diagnosis and management of patients with cancer. This study aimed to investigate the prevalence and burden of pulmonary and extrapulmonary comorbidities in patients diagnosed with lung cancer (LC) and malignant pleural mesothelioma (MPM). Materials and Methods: The data were obtained from official patient records of the Turkish Ministry of Health. Patients diagnosed with either lung cancer (LC) or malignant pleural mesothelioma (MPM) between 2015 and 2018 were included in the study. Comorbidities were classified as pulmonary or extrapulmonary. Results: A total of 74,835 patients with LC and 1678 patients with MPM were included. The burden of comorbid conditions increased significantly in the post-diagnostic period in both males and females across both cancer types. When the two cancer groups were compared with respect to diagnostic periods, comorbidities such as hypertension (HT), phlebitis/venous thrombosis/thrombophlebitis, pulmonary embolism, pneumothorax, and pleural effusion were significantly more prevalent in the MPM group (p < 0.05). Compared with the pre-diagnostic period, the comorbidity risk in LC was highest for pulmonary embolism, ARF, and pneumonia in the post-diagnostic period, whereas renal failure was the most frequent comorbidity in the MPM group (p < 0.001 and p = 0.024). When comparing changes in comorbidity burden between sexes in the lung cancer group, male patients had higher frequencies of pulmonary embolism, pneumonia, pneumothorax, and coronary artery disease than females. In contrast, in the female lung cancer group, the prevalence of chronic renal failure was higher than in males (OR = 2.14 vs. 2.00), whereas acute renal failure was more prominent in the male patient group (OR = 2.64 vs. 1.94). In gender-based comparison of comorbid conditions among patients with MPM, the risk of renal failure was higher in females than in males (CRF and ARF respectively: OR = 2.63 vs. 2.16 and OR = 6.80 vs. 5.44). Additionally, increased rates of COPD were observed in male patients within this group (OR = 1.93 vs. 1.81). Conclusions: Patients with LC and MPM are burdened not only by their primary malignancies but also by a wide spectrum of comorbidities, particularly in the post-diagnostic period. Comprehensive knowledge of comorbid conditions is essential for clinicians to guide clinical decision-making, anticipate disease progression, and optimize treatment strategies, thereby informing national healthcare policies. Future studies incorporating matched control groups or longitudinal designs with standardized surveillance protocols may help conduct better research. 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

(1) Background: Frailty is a major geriatric syndrome associated with adverse health outcomes, while direct assessment of cardiorespiratory fitness (CRF) is often impractical in routine clinical settings. This study investigated the association between estimated cardiorespiratory fitness (eCRF) and incident frailty in middle-aged and older adults from three nationally representative aging cohorts. (2) Methods: We analyzed longitudinal data from the Health and Retirement Study (HRS; 2006–2020) in the United States, the English Longitudinal Study of Ageing (ELSA; 2004–2018) in England, and the China Health and Retirement Longitudinal Study (CHARLS; 2011–2018) in China. Participants aged 50 years or older were included. eCRF was calculated using validated sex-specific non-exercise algorithms. Frailty was assessed using a 30-item Frailty Index (FI), and incident frailty was defined as FI ≥ 0.25. Cox proportional hazards models were used to evaluate the association between baseline eCRF and incident frailty. (3) Results: A total of 8152 participants (3982 women and 4170 men) were included in the longitudinal analysis. Each 1-SD increase in eCRF was associated with a lower risk of incident frailty in HRS (HR = 0.60, 95% CI: 0.54–0.68), ELSA (HR = 0.54, 95% CI: 0.46–0.64), and CHARLS (HR = 0.74, 95% CI: 0.63–0.87). Compared with the low-eCRF group, the moderate- and high-eCRF groups had progressively lower risks of incident frailty across all three cohorts, indicating a graded inverse dose–response relationship. Findings were generally consistent across subgroup and sensitivity analyses. (4) Conclusions: Higher eCRF was associated with a lower risk of incident frailty among middle-aged and older adults across three nationally representative cohorts. As an accessible, non-invasive metric, eCRF may be useful for identifying individuals at elevated risk of incident frailty. Full article

Modern agriculture faces major challenges due to rapid population growth, climate change, and environmental constraints. Advanced polymeric systems for controlled-release fertilizers (CRFs) are essential to address these challenges. Urea is one of the most widely used nitrogen fertilizers; however, its agronomic efficiency is limited by volatilization and losses. In this study, we report a sustainable strategy to encapsulate urea using a matrix derived from industrial sulfur waste and vegetable oil, improving agronomic efficiency while valorizing industrial residues and renewable resources. Through inverse vulcanization, a sponge-like polymer (Bp-SF) was synthesized. Two urea-loaded bio-composites (Bp-SF25U and Bp-SF32U) were also prepared. FT-IR analysis confirmed urea encapsulation and the formation of polymeric structures from sunflower oil. SEM revealed a porous morphology, while contact angle measurements confirmed the hydrophobic nature of the polymer matrix. Release kinetics showed sustained nitrogen release for more than 77 days, reaching approximately 60% cumulative release, governed by diffusion, with a fraction of urea retained within the matrix, potentially enabling prolonged nutrient availability. Pot experiments with maize showed that a lower dose of encapsulated urea (79 mg) produced similar plant growth responses to a higher dose of free urea (92 mg), indicating improved nitrogen use efficiency. These sulfur cross-linked biopolymers represent a promising strategy to enhance urea efficiency while supporting greener fertilization strategies aligned with circular economy principles. Full article

The new energy vehicle (NEV) industry generates massive multi-source heterogeneous data. To overcome traditional database limitations in terminology disambiguation and multi-hop reasoning, this paper proposes a knowledge graph (KG)-based question-answering (QA) architecture. Three primary domain challenges are addressed: First, to tackle the poor semantic extraction of informal diagnostic texts, a deep semantic parsing network (BERT-BiLSTM-CRF) is integrated to extract high-precision knowledge from 150,000 real-world maintenance records. Second, to solve topological redundancy, the Labeled Property Graph (LPG) specification is employed to encapsulate parameters of 2157 vehicle models as internal attributes, significantly streamlining complex multi-hop reasoning. Finally, to enhance limited reasoning capabilities, an intent classification module (TextCNN) automatically translates natural language into graph queries, enabling deep fault tracing across up to five semantic levels. Experimental results demonstrate 98% and 93% accuracy in entity-relation recognition and intent classification, respectively. The resulting KG (8274 nodes, 14,488 edges) establishes a scalable paradigm for intelligent diagnostic reasoning in complex vertical domains. Full article

Thermal monocular depth estimation can provide more robust depth predictions than RGB-based methods under nighttime and adverse weather conditions. However, when trained with projected LiDAR supervision, depth models often retain structural errors in sky regions, long-range areas, and object boundaries because LiDAR measurements are sparse or missing in such regions. To address this limitation, we propose a thermal monocular depth estimation framework that incorporates propagation-based refinement. To make this refinement applicable across different base models, we further design a multi-scale feature adapter that converts heterogeneous multi-scale features with different spatial resolutions and channel dimensions into a unified representation. As a result, the same refinement architecture can be used across different base models without model-specific refiner redesign. On the multispectral stereo (MS2) dataset, the proposed method improves both BTS (big-to-small) and NeWCRFs (neural window fully connected CRFs), reducing the meter-based error metrics SqRel from 0.380 to 0.369 and RMSE from 3.163 to 3.126 for BTS, and reducing SqRel from 0.331 to 0.328 and RMSE from 2.937 to 2.924 for NeWCRFs. Qualitative results further show that the proposed method alleviates mixed-depth artifacts and abnormal depth patterns in regions lacking reliable depth supervision. Full article

Land use change detection (LUCD) serves as a crucial technical cornerstone for natural resource management and ecological environment monitoring, playing an indispensable role in advancing the modernization of national governance capacities. Nonetheless, severe interference from radiometric variations on feature representation readily induces spurious changes and thus a high false alarm rate. Additionally, the challenge of balancing discriminative feature extraction and fine-grained contextual modeling leads to fragmented change regions and missed detection. To address these issues and eliminate the reliance on annotated samples, a novel framework is proposed for unsupervised LUCD, integrating superpixel-based deep feature analysis with a dense conditional random field (CRF). Firstly, relative radiometric correction and band-wise maximum stacking fusion are performed on the bi-temporal images. A simple non-iterative clustering (SNIC) algorithm is adopted to generate homogeneous superpixels with cross-temporal consistency. Then, a deep feature coupling mining mechanism is introduced to implement spatial–spectral feature extraction and in-depth parsing of invariant semantic information. Meanwhile, the difference confidence map based on dual features is constructed using superpixel-level discriminant vectors to enhance the separability. Finally, leveraging homogeneous units with spatial correspondence, a task-specific redesign of a global optimization model is established to achieve the precise extraction of change regions, which incorporates difference confidence, spatial adjacency relationship, and cross-temporal feature similarity into the dense CRF. The experimental results demonstrate that the proposed method achieves an average overall accuracy of over 90% across all datasets with excellent comprehensive performance, striking a well-balanced trade-off in practical applicability. It can effectively suppress salt-and-pepper noise, significantly improve the recall rate of change regions (maintaining at approximately 90%), and exhibit favorable superiority and robustness in complex land cover scenarios. Full article

This study investigates the decomposition kinetics and microplastic residue formation of the polymer-coated controlled-release fertilizers (CRFs) LN40 and Eco-LN40 under simulated photodegradation conditions. Eco-LN40, containing TiO₂ as a photocatalyst, achieved complete decomposition (100 ± 2%) after 60 days of xenon-arc irradiation (p < 0.05), whereas LN40 achieved only 14–31% decomposition. Analytical characterization using TED-GC/MS, FTIR, and Raman spectroscopy confirmed that polyethylene (PE) signals completely disappeared in Eco-LN40 but persisted in LN40, indicating that microplastics did not form and that there was total oxidation into CO₂ and H₂O. SEM–EDS revealed Ti enrichment and surface fragmentation consistent with photoinduced radical oxidation. This study provides qualitative and mechanistic evidence that TiO-catalyzed photodegradation can eliminate polymer residues, mitigate the risk of microplastic contamination in agricultural soils, and support carbon-neutral fertilizer technologies. Full article

To deal with the high standards, strong restrictions, and high repeatability that are inside State Grid small-scale infrastructure projects, this research puts forward a norm-driven generative design method, which conquers the low efficiency, compliance dangers, and semantic breakage that are usual in manual modeling. Taking standards such as Q/GDW 11382.3-2015 as the knowledge origin, we construct an ALBERT-BiLSTM-CRF semantic parsing model and change natural-language clauses into executable design restrictions via normative text pre-processing, BIO sequence marking, and rule triplet mapping. Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base. At the component level, a three-part coding plan and unit module collection are built based on OmniClass and GB/T 51269, which makes semantic consistency and traceability between components and space functions possible. At the system level, a continuous work process is carried out through the Revit API, which covers scheme making, automatic arrangement, and deliverable output. Hence, validation on a real case in a digital operation center for the power system shows that the design time for the third-floor administrative office area was cut from about 20 h to around 4 h, and the first-time solution met all code restrictions, which improves efficiency and compliance in a significant way. The results point out that norm-driven generative design can supply deployable automation and high-quality outputs for small-scale power infrastructure, which provides a sustainable database for digital twins and smart O&M. Full article