Search Results (1,128)

Whānau (family) is a foundational unit of Māori social organisation, and the replacement of Māori family structures with Western nuclear models is widely regarded as among the most significant tools of colonisation. As Māori move toward decolonisation and re-Indigenisation, approaches to family and identity are shifting from imported structures. However, takatāpui and irahuhua (LGBTQ+ and gender-diverse Māori) are rarely explicitly included in these movements. Contemporary framings of whānau within Māori discourse can inadvertently reiterate colonial talking points, particularly regarding binary gender roles, divisions of labour, and the boundaries of what constitutes whānau. Building on takatāpui scholarship and the findings from three separate wānanga (targeted collective conversations) across a 1.5-year period with 18 irahuhua participants, the article examines the contrasts and connections between “coming out” and “coming in” to tradition and whānau. These conversations revealed that when participants enact self-determination and “come in” to their whānau, they demonstrate pathways to strengthen and restore Māori understandings of whānau and challenge the role of historic inquiry in modern Māori politics. Grounded in one wānanga participant’s understanding that “family pressures that make you feel divided [are] just what the coloniser wanted,” this article explores how takatāpui and irahuhua strengthen their communities and demonstrate sovereignty in settler contexts. Full article

Foggy traffic scenes pose significant challenges for object detection because reduced contrast, blurred object boundaries, and the loss of local details weaken discriminative feature representations. These degradations are particularly detrimental to lightweight detectors used in intelligent transportation and vehicle perception systems, where both accuracy and real-time efficiency are required. To address this problem, this paper proposes YOLO-GCM, a lightweight detector-side feature enhancement framework built upon YOLO11n. Instead of relying on an external image dehazing stage, YOLO-GCM improves the internal feature representation of the detector through three complementary modules: a gated additive feature block (GAFB) for adaptive channel-wise feature selection and noise suppression, a context-aware feature enhancement module (CAFEM) for strengthening high-level semantic context, and a multi-scale adaptive fusion (MSAF) module for enhancing cross-scale feature interaction. By integrating these modules into a unified one-stage detector, the proposed method improves detection robustness under low-visibility traffic conditions while maintaining a compact architecture. Experiments on the FoggyCar dataset show that YOLO-GCM achieved 89.81% mAP@0.5 and 67.99% mAP@0.5:0.95, outperforming standard YOLO baselines and dehazing-assisted detection pipelines under a consistent evaluation protocol. Additional evaluation on Foggy Cityscapes further verified the generalization capability of the proposed method under domain shift. The results demonstrate that detector-side feature enhancement provides an effective and efficient alternative to multi-stage dehazing-plus-detection pipelines for foggy traffic object detection. These findings can provide useful guidance for the development of robust and efficient perception modules in roadside monitoring, intelligent transportation systems, and vehicle-assisted driving applications under adverse weather conditions. Full article

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants. Full article

Joint detection of strawberry growth stages and defective fruit is needed for harvest planning and quality screening, but field images make this task difficult because stage-related visual differences are subtle, flowers and early fruits are often small and densely distributed, and occlusion weakens localization reliability. This study develops Multi-Scale Refined Detection and Classification YOLO (MRDC-YOLO), a lightweight detector based on the YOLO11s framework, for this fine-grained detection scenario. The backbone, neck, and detection head are redesigned with three modules: a Multi-Scale Adaptive Edge Enhancement Module (MAEM), a Reparameterized Progressive Feature Aggregation (RPFA) module, and a Decoupled Cross-Scan Head (DCSH). MAEM strengthens boundary and texture responses for visually similar categories, RPFA reduces redundant multi-scale fusion while maintaining features for dense small targets, and DCSH introduces task-aware classification and regression branches with cross-scan-inspired spatial modeling for occlusion-sensitive localization. Experiments on a five-class strawberry dataset containing 5114 images show that MRDC-YOLO achieves 95.63% mAP@0.5 and 82.39% mAP@0.5:0.95. Over YOLO11s, the model yields a 2.06-percentage-point gain in precision and 1.34- and 1.53-percentage-point gains in mAP@0.5 and mAP@0.5:0.95, together with 10.7% fewer parameters and 8.9% lower GFLOPs. These results suggest that MRDC-YOLO improves fine-grained category discrimination and localization while retaining a smaller model size than the YOLO11s baseline. Full article

Maritime search and rescue is an important component of emergency response frameworks and primarily relies on Unmanned Aerial Vehicles (UAVs) for maritime object detection. However, maritime accidents frequently occur in low-visibility environments, such as foggy or low-light conditions, which lead to low contrast, blurred object boundaries, and degraded texture representations. Most existing maritime object detection algorithms are developed for natural light scenes, and their performance deteriorates markedly when deployed directly in low-visibility environments, primarily due to reduced image quality that hinders feature extraction and semantic information aggregation. Although several studies incorporate image enhancement techniques prior to detection to improve image quality, these approaches often introduce significant additional computational overhead, limiting their practical deployment on UAV platforms. To tackle these challenges, this paper proposes a lightweight model built upon a recent YOLO framework, termed Multi-Scale Adaptive YOLO (MSA-YOLO), for maritime detection using UAVs in low-visibility environments. The proposed model systematically optimizes the backbone, neck, and detection head networks. Specifically, an improved StarNet backbone is designed by integrating Efficient Channel Attention (ECA) mechanisms and multi-scale convolutional kernels, which strengthen feature extraction capability while maintaining low computational overhead. In the neck network, a high-frequency enhanced residual block branch is inserted into the C3k2 module to capture richer detailed information, while depthwise separable convolution is utilized to further reduce computational cost. Moreover, a non-parametric attention module is incorporated into the detection head to adaptively optimize features in the classification and regression branches. Finally, a joint loss function that combines bounding box regression, classification, and distribution focal losses is utilized to improve detection accuracy and training stability. Experimental results on the constructed AFO, Zhoushan Island, and Shandong Province datasets demonstrate that, relative to YOLOv11-s, MSA-YOLO reduces model parameters and FLOPs by 52.07% and 41.36%, respectively, while achieving improvements of 1.11% and 1.33% in mAP@0.5:0.95 and mAP@0.5. These results indicate that the proposed method effectively balances computational efficiency and detection accuracy, rendering it suitable for practical maritime search and rescue applications in low-visibility environments. 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

The effects of Mn/RE (Nd, Gd) multi-modification on the microstructure and high-temperature compressive creep properties of Mg–8.0Al alloys were investigated. The dominant intermetallic phases in the as-cast microstructure are β-Mg₁₇4Al₁₂, Al₂(Gd,Nd), Al₁₁(Gd,Nd)₃, Al₈(Gd,Nd)Mn₄, and Al₁₀Mn₂(Gd,Nd). The detailed structures of various intermetallics were revealed by TEM; the results indicate that Mn addition promotes grain refinement and facilitates the precipitation of lath-shaped and spherical β-Mg₁₇Al₁₂ in as-cast Mg–Al–RE alloys, resulting in increases in the tensile strength and elongation of the 1.0Mn alloy by 26.5% and 92.1%, respectively. Additionally, thermally stable micron-scale Al₈(Gd,Nd)Mn₄ and Al₁₂(Gd,Nd)₂Mn₅, along with dynamically precipitated spherical nano-sized AlGd and AlNd particles in the α-Mg matrix, were innovatively observed in compression-crept specimens tested at 200 °C and 60 MPa; these phases play a key role in improving high-temperature creep resistance. A significant finding is that excessive Mn addition deteriorates creep performance, which is attributed to excessive grain refinement and the consequent increase in the contribution of grain boundary sliding during creep. However, the negative effect of grain boundary sliding—caused by grain refinement—on creep performance can be balanced by the strengthening effect of Al–Mn–Gd phases and the dynamic precipitation of nanoscale Al–RE particles. This paper provides new insights for designing Mg–Al–Nd–Gd–Mn alloys with both excellent high-temperature creep resistance and significantly enhanced mechanical properties. Full article

With the development of digital technology and artificial intelligence (AI), numerous studies have focused on the applications and impacts of digital technology in the public sector. However, few studies have explored how frontline public service employees, the core subject of public organizations, can improve their proactive service performance. Based on the model of proactive motivation, this paper investigates the influence of digital leadership on employees’ proactive service performance from a micro perspective, as well as the internal mechanisms and boundary conditions underlying this process. Through an analysis of three-wave questionnaire survey data from 234 employees, this study finds that digital leadership has a positive impact on public employees’ proactive service performance through the serial mediation effects of AI service awareness and AI crafting. Furthermore, as an important boundary condition, employees’ public service motivation strengthens the serial indirect effect of digital leadership on proactive service performance. This paper not only extends the literature on digital leadership by adopting a micro-level perspective within the context of public sector digital transformation but also identifies the individual and contextual antecedents of proactive service performance by examining the interactive effect of public service motivation and leadership. Furthermore, this paper offers valuable implications for the practice of digital transformation in public organizations. Full article

Existing studies on energy tunnels mainly focus on short-term heat transfer and neglect long-term thermal accumulation. This paper establishes a one-dimensional unsteady heat transfer model using Robin boundary conditions, considering air–lining coupled heat transfer and seasonal tunnel air temperature variations. The model is verified with experimental and numerical results, and the relative error is less than 1%. Simulations of 20-year continuous operation show that the host rock temperature presents obvious periodic fluctuations. The thermal influence zone expands rapidly at the initial operation stage and gradually stabilizes. Sensitivity analysis indicates that thermal conductivity, air flow velocity and circulating fluid velocity significantly affect the long-term thermal performance. Higher thermal conductivity speeds up heat diffusion, higher air velocity strengthens convective heat transfer, and higher fluid velocity improves heat exchange efficiency but increases pumping consumption. The model can accurately predict long-term temperature evolution, providing theoretical support for the design and operation optimization of energy tunnels. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

Mg-Al-Ca alloys are attractive low-cost wrought Mg alloys. However, the distinct roles of Al and Ca in regulating deformation-processed microstructures and mechanical properties remain unclear. In this work, Mg–6Al–0.5Ca, Mg–9Al–0.5Ca, and Mg–9Al–1.3Ca (wt. %) alloys were extruded at 250 °C and 300 °C to clarify the composition-dependent microstructure evolution and strengthening mechanisms. Increasing the Al content from 6 to 9 wt. % markedly promoted the formation of fine Mg₁₇Al₁₂ (f-Mg₁₇Al₁₂) and coarse Mg₁₇Al₁₂ particles, whereas increasing the Ca content from 0.5 to 1.3 wt. % promoted the formation of coarse Al₂Ca particles while reducing the density of f-Mg₁₇Al₁₂. Quantitative analysis revealed that f-Mg₁₇Al₁₂ particles refined dynamically recrystallized grains by promoting recrystallization nucleation and pinning grain boundaries while also contributing to Orowan strengthening. The Mg–9Al–0.5Ca alloy exhibited the best strength–ductility balance, with a yield strength of 338 ± 4 MPa, ultimate tensile strength of 396 ± 5 MPa, and elongation of 8.7 ± 1.6% after extrusion at 250 °C. Strengthening calculations indicated that grain-boundary strengthening was the dominant strengthening contribution, while the strength advantage of Mg–9Al–0.5Ca originated from the dual role of f-Mg₁₇Al₁₂ in grain refinement and dislocation obstruction. These findings provide a practical strategy for designing high-strength non-rare-earth Mg–Al–Ca extrusion alloys. Full article

Background: Myocardial scar segmentation from late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images plays an important role in cardiac disease assessment and prognosis evaluation. However, accurate scar annotation is labor-intensive and requires substantial clinical expertise because scar regions are typically small, irregularly shaped, and characterized by ambiguous boundaries. Although scribble supervision provides a more practical alternative to dense annotation by substantially reducing labeling costs, the extreme sparsity of scribbles and the high similarity between scar tissue and surrounding myocardium make accurate weakly supervised segmentation challenging. Methods: To address these challenges, we propose SSMSNet, a novel scribble-supervised framework for myocardial scar segmentation. Specifically, a weakly supervised anatomical segmentation network is first employed to provide reliable myocardial structural priors and suppress irrelevant background interference. Subsequently, a local distance prior map is dynamically generated from scribble annotations, and a corresponding loss is introduced to enhance structural awareness and improve training stability. Meanwhile, by leveraging the spatial correlation between the myocardium and scar regions, teacher–student consistency supervision progressively recovers more complete scar structures from sparse annotations. Furthermore, a detail-aware feature enhancement module strengthens low-level representations through contextual interactions and attention mechanisms, improving the perception of scars with ambiguous boundaries. Results: Extensive experiments conducted on two public cardiac pathology datasets demonstrate that the proposed framework consistently outperforms state-of-the-art scribble-supervised methods and achieves competitive performance compared with fully supervised methods. Conclusions: The proposed SSMSNet effectively alleviates the limitations imposed by scribble annotations by integrating anatomical guidance, local distance priors, and consistency learning. These findings suggest that the framework provides an effective and annotation-efficient solution for myocardial scar segmentation in LGE CMR images. Full article

Mg–Y–Zn alloys have attracted considerable attention for lightweight structural applications; however, the influence of extrusion temperature on microstructural evolution and the underlying mechanisms governing strength–ductility synergy remains insufficiently understood. In this study, a novel YZG921 (Mg–9Y–1.8Zn–1.2Gd–0.5Zr–0.3Ca, wt.%) alloy was fabricated by hot extrusion at temperatures ranging from 480 to 520 °C. The microstructure, mechanical properties, and deformation behavior were systematically investigated using SEM, TEM, EBSD, in situ EBSD, and slip-trace analysis. The results show that extrusion temperature significantly affects the evolution of secondary phases, grain size, and texture intensity. At 500 °C, an 18R-LPSO phase was formed, accompanied by a more homogeneous distribution of secondary phases and the finest grain structure (~3.8 μm), whereas the average grain size remained close to 10 μm for the alloys extruded at 480 °C and 520 °C. Meanwhile, the maximum basal texture intensity decreased from 4.16 to 4.79 m.r.d. to 2.18–2.58 m.r.d. Mechanical testing revealed that the alloy extruded at 500 °C exhibited the optimum strength–ductility balance, with an ultimate tensile strength of 498.4 MPa and an elongation of 13.8%. In situ EBSD analysis showed that the fraction of low-angle grain boundaries increased from ~7% to 43% during tensile deformation, while the average KAM value increased from ~0.5° to 0.88°. Slip-trace analysis further demonstrated that plastic deformation was predominantly governed by basal slip, accounting for approximately 84.2% of the activated slip systems. The superior mechanical performance achieved at 500 °C is attributed to the synergistic effects of grain refinement, LPSO and second-phase strengthening, texture weakening, and sustained strain hardening. These findings provide insights into microstructure–property relationships and offer guidance for the optimization of thermomechanical processing parameters in Mg–Y–Zn alloys. Full article

Metallurgical lifting equipment operates under prolonged heavy-load, high-impact, and complex working conditions. The resulting failure reports contain rich field knowledge applicable to fault diagnosis and predictive maintenance. Nevertheless, reliably extracting traceable, structured knowledge from procedural and implicit maintenance records remains a significant challenge. To address this, the paper proposes a generative information extraction method for large language models (LLMs) that integrates ontology schema with inference chain constraints, targeting knowledge extraction and knowledge graph construction from failure reports of metallurgical lifting equipment, named generative constrained information extraction for operations and maintenance (GCIE-OM). A domain ontology schema is first constructed, defining seven entity types and nine relation types to establish explicit knowledge boundaries for structured LLM generation. An inference chain-assisted structured parsing method, termed IC-ASP, is then designed to guide the model through a sequential extraction pipeline comprising scene identification, scope of entity boundary, inference of relation type, evidence traceability with localization, and triple output. This stepwise process strengthens the model’s capacity to comprehend equipment hierarchies, fault evolution chains, and maintenance action logic. Building on this, ChatGLM or LLaMA serves as the backbone model and is adapted to the target domain via LoRA fine-tuning. Entity alignment and character-level source localization mechanisms are further introduced to establish precise mappings between generated outputs and their textual evidence in the source documents. The extracted results are ultimately converted into standardized knowledge triples and stored in a Neo4j graph database. Based on this, a prototype system for generative information extraction is designed and implemented to demonstrate the practical effectiveness and adaptability of the proposed method. Experimental results show that the proposed method outperforms baseline methods across entity recognition, relation extraction, and structured output quality, providing robust knowledge support for fault tracing and predictive maintenance of metallurgical lifting equipment. Full article

This study aims to develop an instance segmentation model for the joint segmentation of pomelo fruits and stems in complex natural orchard environments, with particular emphasis on slender, small-scale, and easily occluded stem targets. To this end, YOLO-MOD, an improved instance segmentation algorithm based on YOLOv11-seg, is proposed. Specifically, Omni-Dimensional Dynamic Convolution (ODConv) is introduced into the C3k2 module to enhance complex feature representation; a Multi-Scale Dilated Attention (MSDA) module is embedded to improve the multi-scale semantic perception of slender stem regions; and the original upsampling operator is replaced with DySample to strengthen fine-grained boundary recovery. Experimental results show that, compared with the original YOLOv11-seg, YOLO-MOD improves the Box mAP@50 and Mask mAP@50 by 2.9% and 3.9%, respectively. For the Stem class, the Box mAP@50 and Mask mAP@50 increase from 71.9% to 77.8% and from 68.4% to 76.2%, respectively. These results indicate that YOLO-MOD can achieve fine-grained segmentation of pomelo fruits and stems on the dataset used in this study. However, its generalization capability across different orchards, seasons, pomelo varieties, and fruit types still requires further evaluation, and its practical effectiveness in an integrated robotic harvesting system remains to be further validated. Full article