Search Results (3,444)

The uplift of the Qinghai–Tibet Plateau has shaped a unique extreme environment, fostering distinct endemic aquatic organisms. Schizopygopsis pylzovi, a vulnerable endemic fish in the upper Yellow River, is a key model for studying the biogeographic patterns of plateau fish. To assess its genetic characteristics and evolutionary dynamics, we comprehensively evaluated 11 geographic populations of S. pylzovi using two complementary mitochondrial markers, the conserved COI gene and the fast-evolving D-loop region. A total of 142 COI and 143 D-loop sequences were analyzed, and sequences alignment, haplotype network construction, AMOVA, and neutrality tests were performed. AMOVA revealed that genetic variation was mainly distributed within populations, indicating weak population differentiation. Neutrality tests and mismatch distribution analysis suggested historical and recent population expansion events. Our findings highlight the value of joint analysis using COI and D-loop markers in revealing the genetic structure of S. pylzovi, provide new insights into the impact of plateau uplift on fish evolution, and establish a scientific basis for the conservation of this vulnerable species. Full article

Fruit color is a key quality indicator for apples and directly influences their market value. The process of fruit ripening encompasses various physiological and biochemical changes, such as the breakdown of chlorophyll and the buildup of anthocyanins and carotenoids. This study investigated the mechanism of chlorophyll degradation in apple peels using ‘Granny Smith’ varieties. The experiments involving the treatment with methyl jasmonate (MeJA) indicated that a concentration of 10 µM MeJA led to a reduction in chlorophyll degradation, while a higher concentration of 1500 µM MeJA enhanced this degradation, which aligned with the variations observed in the expression of genes associated with chlorophyll degradation. The key chlorophyll degradation gene MdSGR1 was cloned and found to be induced by methyl jasmonate. MdSGR1 encodes a 283-amino-acid protein belonging to the stay-green superfamily. The promoter possesses inducible cis-acting elements that respond to methyl jasmonate, low temperature and light, while the protein is localized to chloroplasts. Overexpression and silencing vectors were constructed. Overexpression of MdSGR1 induced chlorosis in tobacco leaves and ‘Granny Smith’ apple peels, decreased chlorophyll content, and upregulated related gene expression. Conversely, silencing MdSGR1 produced opposite effects. Arabidopsis thaliana plants overexpressing MdSGR1 exhibited low chlorophyll content, reduced photosynthetic rate, upregulated expression of genes associated with chlorophyll degradation. The results of yeast one-hybrid and dual-luciferase reporter assays indicated that the MdMYC2 transcription factor interacts with the promoter region of MdSGR1. In conclusion, MdSGR1 is crucial for the degradation of chlorophyll in apple peel, and it is regulated both by the MdMYC2 transcription factor and different concentrations of MeJA. This study preliminarily elucidated the regulatory mechanism of methyl jasmonate on chlorophyll degradation in fruit peel, and these findings provide an important theoretical basis for controlling degreening and color quality in apple fruit. Full article

Edge-side acoustic monitoring enables animal sound recognition in remote environments, but microcontroller deployment remains constrained by feature extraction, numerical consistency, memory, latency, and energy consumption. This study presents a sensor-based tiny machine learning (TinyML) acoustic monitoring system on an Arduino Nano 33 BLE Sense Rev2 platform, integrating onboard pulse-density modulation (PDM) microphone acquisition, Mel-frequency cepstral coefficient (MFCC) feature extraction, deployment-side standardization, 8-bit integer (INT8) neural-network inference, and edge-side decision output. To reduce training-to-deployment feature drift, consistent frame parameters, mirrored C++ feature operators, and exported standardization parameters are used to align personal-computer-side and microcontroller-side feature representations. A source-isolated seven-class protocol was constructed for six target animal classes and one compound background-noise class. In the single-run baseline comparison, the proposed multilayer perceptron achieved 98.28% test accuracy and 97.21% test macro-F1, while the ten-seed stability analysis yielded 98.64% ± 0.26% test accuracy and 97.87% ± 0.38% test macro-F1. The deployed INT8 model occupied approximately 26.9 KB, with a post-window latency of about 303 ms. System-level input power was 0.783–0.825 W, corresponding to an estimated autonomy of 7.63–8.03 h under the reference battery setting. Full article

The aim of this systematic literature review was to analyse how hydrological–hydraulic modelling, through the assessment of surface stormwater runoff behaviour, can support the participatory management of socio-environmental risks such as flooding, flash floods, and landslides. For this, 31 publications dating from 2015 to 2025 were selected from Scopus, ScienceDirect and Web of Science databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, to examine the importance of integration between modelling and community participation for risk management. The results indicate that, despite recent advances, most studies still prioritise either the technical application of modelling or community participation, without articulating the two approaches in risk analysis and management processes. There is a scarcity of methods that effectively combine local knowledge into the collaborative construction of scenarios and in the continued use of modelling as a tool for monitoring flood risks to disseminate community information. It was observed that studies carried out in developing countries use simpler methods, using community participation as an alternative to the absence of data. In developed countries, however, studies use more advanced methodologies through institutionalised processes. In contexts marked by high vulnerability, the integration of community participation and technical tools, such as hydrological–hydraulic modelling, represents a promising pathway toward more equitable and efficient risk management practices, aligning with sustainability agendas such as the Sustainable Development Goals (SDGs). Full article

Chinese cloze question generation for educational assessments requires identifying gap phrases that accurately reflect key knowledge points, posing significant challenges to automated systems. We observe that the syntactic boundaries revealed by part-of-speech (POS) tags closely align with the semantic boundaries of target gap phrases. Motivated by this observation, we propose a multi-task learning framework in which gap phrase identification serves as the primary task and POS tagging as a complementary auxiliary task. The two tasks share a common BERT-BiLSTM encoder, enabling mutual reinforcement of both syntactic and semantic representations through joint training. To further capture the interaction between label semantics and contextual word representations, we introduce a label-attention mechanism that models dependencies between the global word sequence and candidate label embeddings. Additionally, we construct a refined POS tag subset by excluding categories whose boundaries show no alignment with gap phrase boundaries, thereby strengthening the correspondence between the two tasks. Evaluated on a real-world dataset of 20.5K questions spanning five academic disciplines, our method achieves an F1 score of 65.85%, with a Recall of 67.79%, representing improvements of 2.12% and 4.35% over the prior state-of-the-art, respectively. These results demonstrate that exploiting the alignment between syntactic and semantic structures through joint learning is effective for generating educationally meaningful fill-in-the-blank questions. Full article

This paper investigates folding as a practicable design methodology in response to the combined requirements of complex sites and public programs. A sloped waterfront community theater in Boston is used as a test case, where a parametric workflow in Rhino/Grasshopper is employed to translate continuous surfaces, via panelization, into buildable systems constrained by curvature and developability. In the Boston community theater case study, diamond panels are employed for the primary enclosure and seating; stepped panels organize circulation across the slope; and triangular closures resolve edge conditions and tolerances. Fold lines simultaneously function as legible paths, stitching exterior and interior into a continuous sequence. Parameters are used to align lines of sight, gradients, and drainage with structural supports, thereby demonstrating a traceable linkage from geometry to construction and operation. The findings reveal that folded geometries establish continuous linkages among topography, circulation, and program; that fold lines function as force paths, drainage organizers, and edge closures; and that interstitial layers between folded interfaces facilitate transitions between performance and everyday modes, thereby sustaining public use. The study proposes a reusable “folding–parametric–panelization–structural integration” framework, providing a transferable technical pathway for community-scale public architecture. Full article

Dimensional and transdiagnostic models have become central to contemporary efforts to move psychiatric nosology beyond DSM/ICD categories. This shift reflects persistent limitations of categorical syndromes as final biological targets, including within-diagnosis heterogeneity, cross-diagnostic comorbidity, developmental instability, and incomplete alignment with underlying mechanisms. This article examines a central unresolved problem in this transition: when, if ever, a descriptive or predictive psychiatric dimension can be interpreted as a candidate disease axis. We conducted a conceptual synthesis of major dimensional and transdiagnostic frameworks, including Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP), the general psychopathology factor, cross-disorder genomic models, clinical staging approaches, and data-driven subtyping. The analysis separates three levels of inference that are often conflated in psychiatric research: descriptive structure, predictive utility, and disease-level biological validity. The synthesis identifies a recurrent inferential error in which reproducible factors, clusters, or classifiers are prematurely treated as evidence of disease architecture. Such constructs may describe real covariance patterns or improve prognostic prediction without establishing biological validity. We propose an eight-domain hierarchical framework for promotion to candidate disease-axis status, organized into four core gatekeepers—replication across cohorts, ascertainment, and methods, developmental coherence, incremental prognostic value beyond diagnosis and nonspecific severity, and discriminability from nonspecific severity—and four supporting/disciplining domains: cross-level convergence, mechanistic constraint, clinical leverage, and explicit falsifiability/boundary conditions. On this basis, middle-level transdiagnostic spectra and selected cross-disorder genomic liabilities appear more defensible as candidate disease axes than highly global or weakly specified constructs. Psychiatry was justified in turning toward dimensional models, but dimensionality alone does not confer biological validity. The key task is not to choose between categories and dimensions, but to define the evidential thresholds under which dimensional constructs warrant ontological promotion. Full article

The article presents an original approach to automatic musical key detection, combining Constant-Q Transform (CQT) analysis with the Triple Composite Signature of Fifths (TCSF). The method’s novelty lies primarily in the construction of the Signature of Fifths (SF), which is grounded in fundamental principles of music theory and builds on earlier SF-based studies. The proposed approach aims to preserve the algorithmic simplicity typical of SF approaches while strengthening their key advantages. In addition, the method reflects the analytical approach of experienced musicians by assigning greater importance to the initial and final sections of a piece. The use of CQT enables efficient audio analysis and offers a practical compromise between frequency resolution and alignment with the pitch-class representation. Experiments conducted on Franz Schubert’s songs from the Winterreise song cycle and Frédéric Chopin’s Preludes, Op. 28, confirm the effectiveness of the proposed algorithm, achieving 87.5% and 79.2% key-detection accuracy, respectively. The obtained results demonstrate that the proposed method is competitive with tonal profile-based key-detection approaches. Full article

To address the limitations of vision-only defect detection, image–semantic misalignment, and spatial-logic conflicts in complex substation inspection scenarios, this paper proposes a camera-sensor-based multimodal defect sensing framework with cross-modal feature mapping for substation equipment monitoring. The proposed framework integrates field inspection images acquired by camera sensors, defect textual descriptions, and equipment topology knowledge and establishes a unified domain-adaptive pre-training–bidirectional cross-modal mapping–hierarchical reasoning workflow. First, a Contrastive Language–Image Pre-training (CLIP)-based domain-adaptive pre-training strategy is developed to enhance the representation of equipment categories, defect attributes, and inspection-scene semantics. Second, a bidirectional cross-modal feature mapping network is constructed to model fine-grained interactions between candidate visual regions and textual semantics, where uncertainty-aware fusion and prototype constraints are introduced to improve semantic alignment and defect discrimination. Third, a hierarchical neuro-symbolic reasoning module incorporates equipment topology and spatial rules for posterior verification, logical consistency checking, and false-positive suppression. Experiments on a substation inspection image dataset demonstrate that the proposed method achieves 90.8% mAP@0.5, 68.7% mAP@0.5:0.95, and 89.4% F1-score, outperforming mainstream and recent detection models. Full article

Rainstorm-induced disaster chains are characterized by high suddenness, immense destructive power, and complex chain propagation mechanisms. Traditional static assessment methods rely on fixed parameters and struggle to depict the dynamic evolution of such disasters. Existing dynamic models are mostly based on predefined structures and lack the capability to integrate multi-source data and quantify uncertainty, thereby constraining the accurate prediction of rainstorm disaster chains. To address these issues, this study proposes a rainstorm disaster chain prediction model (SW-DBN) that integrates a large language model (LLM)-optimized sliding window mechanism with a dynamic Bayesian network (DBN). The model first performs dynamic segmentation and feature extraction on multi-source time-series data through the sliding window mechanism and constructs an LLM-driven module for semantic understanding of multi-source information and latent parameter mining. By leveraging the LLM’s in-depth analysis of data pattern variations within the window, the model excavates latent parameters, adaptively adjusts the DBN network topology, and feeds back to optimize the window width and sliding step, thereby maintaining adaptive alignment between the sliding window’s feature extraction and the dynamic evolution of the disaster chain. Ultimately, the cascade propagation process of the rainstorm disaster chain is modeled, reasoned, and validated through the DBN, forming an integrated prediction framework of “perception–reasoning, dynamic regulation, and cascade verification.” A case study in the Xi’an area demonstrates that the proposed model can effectively simulate the temporal evolution of rainstorm disaster chains. The average prediction accuracy for four key types of disaster nodes reaches 84.8%, representing an improvement of 7.5 percentage points over the standard DBN model, with clear advantages in early warning timeliness for critical nodes. The proposed model provides technical support for the probabilistic prediction of rainstorm disaster chains and disaster prevention decision-making, featuring both dynamic adaptability and interpretability. Full article

Contemporary societies are characterized by increasing role multiplicity and accelerated social change, intensifying identity-related strain and inter-role conflict. Although role theory, narrative identity research, and psychological flexibility frameworks have independently advanced the understanding of psychological adaptation, an integrative structural model explaining how life domains are hierarchically organized and reorganized over time remains underdeveloped. This manuscript introduces Casa Vital (Vital House), a dynamic structural model that conceptualizes identity as a hierarchical architecture of interdependent life domains organized around a central integrative function. The model proposes three core constructs: structural coherence, structural modes (rigidity/flexibility) and self-directed agency, and argues that psychological adaptation depends not only on emotional regulation or narrative coherence but also on the capacity to reorganize domain hierarchies in alignment with personal values and contextual demands. By positioning identity at a meso-structural level of analysis, the framework integrates sociological, narrative, and contextual behavioral traditions while offering testable hypotheses and a falsifiable research agenda. Casa Vital expands the current models of adaptation by introducing hierarchical structural reorganization as a central component of identity functioning in complex contemporary contexts. Full article

Industrial control systems (ICSs) are cyber–physical environments in which physical process data and network communication data are generated simultaneously. Existing studies have mainly focused on either sensor-based or network-based anomaly detection, making it difficult to capture diverse attack indicators and motivating the use of multimodal methods that can leverage complementary information from both modalities. In this paper, we propose an unsupervised multimodal anomaly detection framework for ICSs that jointly uses sensor and network modalities. For each modality, autoencoder-based single-modality models are trained in an unsupervised manner, and their anomaly scores and latent feature vectors are extracted. These outputs are temporally aligned to construct a time-aligned multimodal table, which is then used to implement and compare two fusion strategies: anomaly score fusion and latent feature fusion. In latent feature fusion, aligned modality-specific latent features are combined with canonical correlation analysis (CCA)-derived cross-modal correlation features. The experimental results showed that latent feature fusion achieved stable performance across multiple sensor–network encoder combinations. In particular, the gated recurrent unit–convolutional neural network (GRU–CNN) combination achieved the best F1-score of 0.9166 and ROC-AUC of 0.9795. In addition, the complementarity analysis showed that latent feature fusion recovered some missed detections by integrating complementary sensor and network evidence. These results demonstrate that latent feature fusion is an effective multimodal strategy for ICS anomaly detection. Full article

In recent years, an essential approach for promoting and implementing efficient sustainable construction practices has been considered through the integration of Building Information Modeling (BIM) and Life-Cycle Assessment (LCA). The introduction of OpenBIM, which is characterized by its collaborative and interoperable nature, offers an ideal framework to enhance this integration. This paper conducts a systematic review of the literature concerning the practices applied to integrate BIM and LCA, focusing on the present trends, challenges, and opportunities as well as on how the concept of OpenBIM can be applied to tackle the identified issues and gaps. Based on an intense review of the literature to identify the ways currently used to exchange data, this paper proposes a robust framework to create Information Delivery Specifications (IDS) as a solution to the identified gaps to attain an effective implementation, ultimately contributing to sustainable buildings’ practices and enhancing the integration of OpenBIM and LCA. OpenBIM emphasizes interoperability and collaboration by using open standards like Industry Foundation Classes (IFCs), which, when combined with LCA, offer a powerful method for the practice of sustainable building and provide a transparent evaluation of the environmental impacts of building materials and processes. This paper explores the definitions, key concepts, types of the exchanged data, and methods of integration and therefore provides insights into their potential in addressing the gaps that the construction industry is currently facing. The framework of integrating OpenBIM and LCA will be developed as a tool; therefore, it will combine an automated validation option by using IDS, create an enriched IFC file(s), dynamically map the data to an external LCA repositories, and incorporate feedback and reporting mechanisms. All those will be combined to address the most persistent shortcomings in the reviewed studies related to the integration of BIM and LCA. The framework will promote a holistic approach covering the early design benchmark to the detailed Whole Building LCA (WBLCA), including the operational and end-of-life phases. This next-generation workflow will align closely to the principles of OpenBIM, leading to improvement in the efficiency, accuracy, and deeper understanding of the environmental impacts by stakeholders over the construction lifecycle of buildings. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article

In engineer-to-order (ETO) manufacturing environments, the high variability of final product configurations makes it difficult to consistently estimate material consumption and, consequently, to define appropriate inventory control policies. This paper proposes a data-driven framework based on unsupervised learning to identify product typologies from historical manufacturing orders in a real industrial context. The approach employs a fuzzy k-prototypes algorithm to cluster mixed-type data, allowing the simultaneous treatment of numerical and categorical variables. In the case study, the proposed crisp-BOM-based scenario achieved a 28.67% reduction in line-side WIP and a 10.79% reduction in linear storage space, corresponding to the release of approximately two to three assembly stations. From the resulting fuzzy memberships, probabilistic bill of materials (BOM) structures are constructed, capturing the inherent variability of material consumption across different product configurations. A defuzzification procedure is then applied to obtain a crisp BOM representation suitable for operational decision-making. Additionally, a material versatility indicator based on entropy is introduced to quantify the dispersion of each material across product typologies. This indicator, together with the estimated consumption per cluster, is used as input for an analytical inventory model that supports the classification of materials into kanban or kitting policies. The methodology is validated using real data from a high- and medium-voltage switchgear manufacturing plant, comprising over 60,000 order–material observations. The results show that the proposed framework enables a more structured characterization of material behavior, reducing reliance on planner experience and improving the consistency of inventory policy decisions. From an industrial perspective, the approach provides a practical and scalable tool for aligning inventory strategies with the actual consumption patterns of ETO systems. Full article