Search Results (150,295)

Employing a 4 (opponent type) × 2 (communication condition) between-subjects design, the study recruited 194 valid human participants to complete three rounds of game tasks. Results revealed: (1) The type of game counterpart exerted a significant main effect on participants’ remaining funds (F(3, 185) = 3.179, p = 0.025). Human participants retained significantly more funds when the counterpart was a real large model compared to other groups. (2) A significant interaction existed between the type of game counterpart and communication conditions (F(3, 185) = 3.318, p = 0.021). Specifically, when the opponent was a fake AI model (presented as human but actually an AI), human participants’ remaining funds were significantly higher under the communication condition than without communication (p = 0.012). This indicates that communication can promote rational decision-making in identity mismatch scenarios by providing additional behavioral cues. In the fake-human group (informed as human but actually AI), a numerical trend toward increased funds was also observed under communication conditions, though it did not reach statistical significance (p = 0.159); (3) The moderating effect of social value orientation did not reach significance. These findings extend the application of the theory of mind in human–machine games, revealing the complex influence mechanism of identity perception and communication dynamics on rational decision-making. Full article

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace manufacturing. This paper presents a structured literature review on the automated assembly of large-scale aerospace components, summarizing advances in three core domains: pose adjustment and positioning mechanisms, digital measurement technologies, and trajectory planning and control. Particular emphasis is placed on two cross-cutting themes: measurement uncertainty analysis and flexible assembly, which are critical for high-quality docking. The review classifies pose adjustment mechanisms into four categories (NC positioners, parallel kinematic machines, industrial robots, and novel mechanisms) and digital measurement into five branches (vision metrology, large-scale metrology, measurement field construction, uncertainty analysis, and auxiliary techniques). It also outlines five trajectory planning and control routes, covering traditional methods, multi-sensor fusion, digital twins, flexible assembly, and emerging intelligent approaches. The analysis reveals that current research suffers from fragmentation among mechanism design, metrology, and control, with insufficient integration of uncertainty propagation and flexible deformation modeling. Future systems will rely on heterogeneous equipment collaboration, uncertainty-aware closed-loop control, high-fidelity flexible modeling, and digital twin-driven decision-making. This review provides a unified framework and a technical reference for developing reliable, flexible, and scalable automated assembly systems for next-generation aerospace structures. Full article

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

Advanced thermal management applications, including electronics cooling, battery systems, and micro heat exchangers, are increasingly requiring thermally conductive yet flexible polymer composites. Composite films containing total nanofiller loadings of 2.5, 5, 7.5, and 10 wt.% were systematically characterized using SEM, TGA, DSC, TT, and SSTM following ASTM C177-19. SEM analysis confirmed uniform dispersion and effective network formation of MWCNTs and GNPs within the TPU matrix at higher filler loadings. Thermal stability improved significantly, with the degradation onset temperature increasing from 319.2 °C for pure TPU to 369 °C for the TPU/MWCNT/GNP (90/5/5 wt.%) composite. DSC results revealed enhanced glass transition and melting temperatures, indicating improved thermal resistance and crystallinity. Mechanical testing showed a substantial increase in Young’s modulus, reaching 72.5 MPa for the 90/5/5 wt.% composite, corresponding to a 286.66% improvement over pure TPU. Most notably, steady-state thermal conductivity increased dramatically from 0.20 W/mK for pure TPU to 1.533 W/mK for the 90/5/5 wt.% composite, representing a 666.50% enhancement. The experimental results closely aligned with percolation-based theoretical models at higher filler concentrations. Overall, the developed hybrid nanofiller TPU composites demonstrate a synergistic improvement in thermal conductivity, mechanical strength, and thermal stability, making them promising candidates for flexible thermal management components in electronics, automotive, renewable energy, and biomedical applications. Full article

This study addresses virtual metrology for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework that reformulates the task as the prediction of operating-condition-level typical yield. First, physically relevant features are retained based on process knowledge and analyzed using Pearson correlation, Spearman correlation, and mutual information. We then perform multidimensional equal-frequency binning to partition the observable feature space into locally homogeneous operating condition groups, and define the within-bin median yield as the typical yield, thereby constructing an operating condition dictionary. Based on this dictionary-based representation, low-yield-oriented sample weighting is combined with nested cross-validation and Bayesian optimization for model comparison and hyperparameter tuning. Using desensitized production data from an electronic component taping process, the results under this representation show more stable prediction than direct modeling on unbinned batch samples while also improving tail-oriented fitting relative to unweighted baselines. These findings suggest that, for partially observable manufacturing data, operating condition stratification provides a practical basis for stabilizing VM prediction, while low-yield-oriented sample weighting further improves sensitivity to the low-yield tail, supporting picture yield early warning and process-level decision making. Full article

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency. Full article

Aging and obesity accompanied with hormonal disequilibrium represent critical, inter-related risk factors for breast cancer, significantly influencing disease incidence, progression, and therapeutic outcomes. This review aims to elucidate the multifaceted biological mechanisms linking obesity and aging to breast carcinogenesis, with a particular focus on the emerging therapeutic and preventive potential of postbiotics as molecules targeting cellular events important for cancer growth and responsiveness. Despite continuous advancement, breast cancer therapy still poses several challenges, such as treatment-induced acquired resistance, which is boosted by the inflammatory phenotype of senescent cancerous cells, as well as undesired side effects resulting from the destruction of normal cells. Such a complex background of breast carcinogenesis and oncotherapy resistance opens avenues to search for new preventive approaches and adjunctive treatment regimens. Postbiotics demonstrate a variety of benefits due to their selective antineoplastic activity, as well as the cytoprotective potential associated with antioxidant, anti-inflammatory and anti-senescent properties. Pleiotropic effects of postbiotics make them a promising tool for counteracting cellular and physiological disturbances that favor breast cancer development, including age- and obesity-related factors. They are prospective adjunctive agents in oncotherapy, albeit their efficacy and safety need to be thoroughly evaluated in clinical studies prior to implementation in routine treatment modes. Full article

Frontal sinus fractures typically reflect high-energy trauma and must be evaluated and treated carefully to avoid long-term problems including contour deformity, sinus dysfunction, cerebrospinal fluid (CSF) leakage, chronic sinusitis, and mucocele formation. This article outlines frontal sinus anatomy, diagnostic pathways, and evolving treatment concepts in detail. An anatomically driven treatment algorithm is emphasized, with a focus on preservation of sinus function whenever possible and preference for conservative management. Advanced procedures, such as endoscopic sinus surgery and cranialization, are reviewed in the context of managing more severe injuries. Key points: (1) Clinical decision-making in the management of frontal sinus fractures is best guided by evaluating the status of the anterior table, posterior table, and nasofrontal outflow tract, with treatment options ranging from nonoperative care to open or endoscopic surgery. (2) Improvements in endoscopic techniques, combined with evidence supporting less aggressive strategies, have shifted management toward more conservative approaches, reserving open procedures for higher-grade injuries. (3) Extended follow-up is essential to identify delayed problems such as mucoceles, chronic sinusitis, frontal bone osteomyelitis, and contour irregularities. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

In late Ming China, landscape (shanshui 山水) painting could function not only as a scenic representation but also as a pictorial means of making sacred space perceptible. This article examines Wu Bin’s hanging scroll Fanghu Tu 方壺圖 (1626; Palace Museum, Beijing) and asks how the painting renders Daoist sacred space visible through relations of distance, access, concealment, and uneven disclosure. To avoid treating “Daoist aesthetics” as a general label, the analysis uses schema and pictorial organization as limited descriptive terms for the structuring of spatial experience within the image. The close reading identifies two recurrent pictorial formations brought into relation in Fanghu Tu: a sea-boundary, distant-view configuration that emphasizes separation and delay, and a pavilion-centered enclosure that produces a more concentrated middle field. It then shows how layered waves and broken shoreline, cloud and mist, middle-zone enclosure, and the thinning legibility of the upper peaks prevent the scene from stabilizing into a single resolved destination. Read in relation to late Ming discussions of cultivated “strangeness” (qi 奇) in landscape painting, these features suggest that Daoist sacred space in Fanghu Tu takes shape as an uneven and mediated experience, structured through provisional concentration, interrupted visibility, and renewed distance. The article argues that late Ming landscape painting could render Daoist-inflected sacred spatial experience visible not only through iconography, but also through the pictorial distribution of visibility, access, and reorientation. Full article

The main objective of this study is to produce a systematic literature review that analyses key performance indicators (KPI) in the context of efficient and sustainable building renovation. Efficiency and sustainability, in combination with building renovation, are important themes due to the increasing need for creating sustainable renovations worldwide. The identification and monitoring of KPIs is fundamental in decision-making processes, but also in the monitoring of short-term and long-term project goals. In the current academic literature, existing research gaps, especially in the social aspects of sustainability and research, have also been analyzed in terms of regional differences in the approach to each KPI. The systematic literature review examined 29 studies published between 2014 and 2024, based on a literature search conducted in 2024, using databases such as Scopus and Web of Science, with the final search performed in June 2024. The inclusion criteria focused on peer-reviewed studies addressing KPIs in sustainable building renovation, while studies not directly related to renovation processes or lacking KPI analysis were excluded. The research results show that the majority of studies focus on economic and environmental factors, which are the most commonly addressed, while research on other KPIs is significantly behind. The results were synthesized using a qualitative comparative analysis of identified KPI categories. This study also highlights the importance of addressing effective and sustainable renovation for historic buildings with a focus on heritage preservation and the need to further analyze the use of KPIs with a focus on historic buildings. The limitations include the limited number of studies and the underrepresentation of social sustainability aspects. Full article

Perhaps one of the most defining ‘techniques of the body’ for human beings is bi-pedal walking. This study brings together studies in socio-cultural anthropology to reflect on the nature of walking as a field method in different social-environmental contexts. The study offers an account of walking in relation to natural history, urban studies and contemporary conservation science. How has walking served as a field method in different knowledge-making contexts, and how does it afford an experiential way of being and belonging (or not) in urban and rural settings? By reflecting on such themes, this paper sheds light on the many ways that people walk, and the places, physical and metaphorical, that it takes them and allows them to discover, reveal, and understand. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article

Considering escalating global climate change, flood disaster risk assessment for architectural heritage must evolve from static models toward dynamic adaptive systems. This paper proposes a dynamic evaluation model based on the Full-Life-Cycle perspective, dividing disaster progression into three phases: pre-disaster, during-disaster, and post-disaster. This system constructs a dual-track indicator system encompassing Exposure and Vulnerability. By integrating the CRITIC objective weighting method with the G1 subjective ranking approach, the model enables dynamic weight adjustment according to disaster phase. A case study of 392 cultural heritage sites in Beijing reveals that during the disaster phase, 20 sites experienced a risk level increase in two or more tiers, with 13.7% directly entering high-risk status. This finding demonstrates the spatiotemporal evolution of flood risks. The weight for Road Network Density exhibited a substantial increase from 0.046 pre-disaster to 0.153 post-disaster, a 169.5% rise, underscoring its dynamic responsiveness. The findings demonstrate that the proposed model is effective in identifying high-risk heritage sites and dynamically capturing key targets experiencing rapid risk escalation within the disaster chain. These results provide quantitative evidence to support the implementation of phased targeted protection measures and emergency decision-making. Full article