Search Results (51,092)

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

This study investigates the technological and chemical causes of early zinc-coating degradation on cold-formed steel sections produced from DX51D+Z140 galvanized coils. Commercially manufactured products exhibiting early corrosion symptoms were used in this study. The entire processing route, which included strip preparation, cold rolling, hot-dip galvanizing, passivation, multi-roll forming, storage, and transportation to customers, was analyzed with respect to the residual surface chemistry and process-related deviations that affect the coating integrity. Thirty-three specimens were examined using electromagnetic measurements of coating thickness. Statistical analysis based on the Cochran’s and Fisher’s criteria confirmed that the increased variability in zinc coating thickness is associated with a higher susceptibility to localized corrosion. Surface and chemical analysis revealed chloride contamination on the outer surface, absence of detectable Cr(VI) residues indicative of insufficient passivation, iron oxide inclusions beneath the zinc coating originating from the strip preparation, traces of organic emulsion residues impairing wetting and adhesion, and micro-defects related to deformation during roll forming. Early zinc coating degradation was shown to result from the cumulative action of multiple technological (surface damage during rolling, variation in the coating thickness) and environmental (moisture during storage and transportation) parameters. On the basis of the obtained results, a methodology was proposed to prevent steel product corrosion in industrial conditions. Full article

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

Accurate measurement of material mechanics parameters is crucial for evaluating process quality and product reliability and is a major challenge in the development of 3D heterogeneous integration technology. Aiming to perform high-accuracy measurements of the elastoplastic nonlinear constitutive parameters of microelectronic materials using the nanoindentation testing technique, we take advantage of a neural network to construct a forward characterization model to characterize these response characteristic parameters for different materials, design an improved algorithm for obtaining a reverse iterative solution of the forward characterization model, and develop a material mechanics parameter measurement method to solve overdetermined equations using the least-squares method. This method was further improved by addressing the issues of algorithm stability and solution uniqueness, achieving high-precision and fast reverse solutions for elastoplastic constitutive parameters. The relative error of the material parameters is less than 3% (95% confidence interval), the maximum error is less than 8%, and the inversion convergence error of the key indentation response characteristic parameters is less than 0.1%. The difference between the measured material parameters and the theoretical model in the influence on the process stress of TCV (through ceramic via) products is verified through finite element simulation. Full article

Phosphogypsum (PG) is the main by-product of wet-process phosphoric acid production. Its annual global production reaches about 200 million tons, yet its utilization rate remains low. Consequently, long-term stockpiling of large PG volumes poses immense pressure to the ecological environment. To mitigate negative environmental impacts, the utilization of PG is imperative. Despite progress in PG utilization and 3D-printing technology, there is still a significant lack of understanding about the synergistic activation mechanisms in multi-solid-waste systems. In particular, the composition design, microstructure evolution, and structure–property relationships of 3D-printed PG-based composites are not well-studied, which limits their high-value engineering applications. Three-dimensional-printed phosphogypsum concrete (3DPPGC) is proposed here, promoting PG resource utilization by leveraging the expanding applications of 3D-printed concrete (3DPC). However, the strength of 3DPPGC needs to be enhanced to meet engineering requirements. This study designed the mix proportion of 3DPPGC and fabricated the corresponding test specimens. The optimal Cement Replacement Ratio (CRR) was determined through experimental testing, and the mechanism behind the strength enhancement of the 3DPPGC was elucidated. The results indicated that the 3DPPGC’s mechanical properties peaked at the 70% CRR. Compared with cast specimens, 3DPPGC exhibited a 1.52% increase in 28-day flexural strength in the y-direction, reaching 4.69 MPa. The early-age compressive strength, flexural strength, and later-age compressive strength of 3DPPGC were significantly enhanced when PG, blast-furnace slag (BS), fly ash (FA), and silica fume (SF) were used to partially replace cement. This study provides a theoretical and experimental basis for the large-scale, high-value application of PG in intelligent construction. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

This study utilises mobile augmented reality (AR) to enhance our understanding of multiword expressions (MWEs) and emphasise that linguistic diversity is part of cultural heritage. The main objective was to implement and evaluate the impact of a multilingual AR resource (in Moldovan, English, Russian, and Spanish) in educational settings and to identify a corpus of MWEs located in Spain. The research was conducted by applying a marker-based AR system in five academic subjects involving N = 220 undergraduate students enrolled in education degrees. Data were collected through two surveys, using both qualitative and quantitative methods that combined descriptive statistics with content analysis. Large Language Models (LLMs) were used to assist with data coding, complemented by iterative human validation. The findings revealed that the application was highly positively received, with 94% of participants acknowledging its usefulness and 83% expressing satisfaction. Furthermore, this study identified a teaching–learning procedure to enhance linguistic diversity in classrooms. Overall, the results suggest that mobile AR constitutes an effective and inclusive pedagogical tool that fosters active learning as a multimodal learning process and provides valuable localised MWE data to support future developments in corpus annotation. Full article

We propose a novel clothing application technique designed for story visualization framework where various characters appear wearing a wide range of outfits. To achieve our goal, we extend a Virtual Try-On framework for synthetic garment fitting. Conventional Virtual Try-On methods are limited to generating images of a single person wearing a restricted set of clothes within a fixed style domain. To overcome these limitations, we apply an improved Virtual Try-On model trained with appropriately processed datasets, enabling the generation of upper and lower garments separately across diverse characters and producing images in four distinct styles: photorealistic, webtoon, animation, and watercolor. Our system collects character images and clothing images and performs accurate masking of garment regions. Our system takes a style-specific text prompt as input. Based on these inputs, garment-specific conditioning is applied to synthesize the clothing, followed by a cross-style diffusion process that generates Virtual Try-On images reflecting multiple visual styles. Our approach significantly enhances the adaptability and stylistic diversity of Virtual Try-On technology for story visualization applications. Full article

Polychrome sculptures are complex, multilayered artifacts that embody the intersection of artistic craftsmanship, material science, and cultural heritage. Over the past two decades, the study of material identification in polychrome sculptures has shown marked interdisciplinary development, driven by advances in analytical technologies that have transformed how these objects are studied, enabling high-resolution identification of pigments, binders, and structural substrates. This review synthesizes key developments in the identification of polychrome sculpture materials, focusing on the integration of non-destructive and molecular-level techniques such as XRF, FTIR, Raman, LIBS, GC-MS, and proteomics. It highlights regional and historical variations in materials and craft processes, with case studies from Brazil, China, and Central Africa demonstrating how multi-modal methods reveal both technical and ritual knowledge embedded in these artworks. The review also examines evolving research paradigms—from pigment identification to stratigraphic and cross-cultural interpretation—and discusses current challenges such as organic material degradation and the need for standardized protocols. Finally, it outlines future directions including AI-assisted diagnostics, multimodal data fusion, and collaborative conservation frameworks. By bridging scientific analysis with cultural context, this study offers a comprehensive methodological reference for the conservation and interpretation of polychrome sculptures worldwide. Full article

Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat source models overestimating the melt pool width and volume heat source models underestimating the melt pool depth, this study constructs a three-dimensional transient heat conduction finite element model based on ANSYS Parametric Design Language (APDL) to simulate the evolution of the temperature field and melt pool geometry under different laser parameters. First, the temperature fields and melt pool morphology and dimensions of four heat source models—Gaussian surface heat source, volumetric heat source models (rotating Gaussian volumetric heat source, double ellipsoid heat source), and a combined heat source model—were investigated. Subsequently, a dynamic heat source model was proposed, combining a Gaussian surface heat source with a rotating volumetric heat source. By dynamically allocating the laser energy absorption ratio between the powder surface layer and the substrate depth, the influence of this heat source model on melt pool size was explored and compared with other heat source models. The results show that under the dynamic heat source, the melt pool width and depth are 128.6 μm and 63.13 μm, respectively. The melt pool width is significantly larger compared to other heat source models, and the melt pool depth is about 17% greater than that of the combined heat source model. At the same time, the predicted melt pool width and depth under this heat source model have relative errors of 1.0% and 5.5% compared to the experimental measurements, indicating that this heat source model has high accuracy in predicting the melt pool’s lateral dimensions and can effectively reflect the actual melt pool morphology during processing. Full article

The rapid advancement of sequencing technologies has led to the generation of complex multi-omics data, which are often high-dimensional, noisy, and imbalanced, posing significant challenges for traditional machine learning methods. The novelty of this work resides in the architecture-level integration of autoencoders with Generative Adversarial Network (GAN) and Conditional Tabular Generative Adversarial Network (CTGAN) models, where the autoencoder is employed for latent feature extraction and noise reduction, while GAN-based models are used for realistic sample generation and class imbalance mitigation in multi-omics cancer datasets. This study proposes a novel framework that combines an autoencoder for dimensionality reduction and a CTGAN for generating synthetic samples to balance underrepresented classes. The process starts with selecting the most discriminative features, then extracting latent representations for each omic type, merging them, and generating new minority samples. Finally, all samples are used to train a neural network to predict specific cancer outcomes, defined here as clinically relevant biomarkers or patient characteristics. In this work, the considered outcome in the bladder cancer is Tumor Mutational Burden (TMB), while the breast cancer outcome is menopausal status, a key factor in treatment planning. Experimental results show that the proposed model achieves high precision, with an average precision of 0.9929 for TMB prediction in bladder cancer and 0.9748 for menopausal status in breast cancer, and reaches perfect precision (1.000) for the positive class in both cases. In addition, the proposed AE–CTGAN framework consistently outperformed an autoencoder combined with a standard GAN across all evaluation metrics, achieving average accuracies of 0.9929 and 0.9748, recall values of 0.9846 and 0.9777, and F1-scores of 0.9922 for bladder and breast cancer datasets, respectively. A comparative fidelity analysis in the latent space further demonstrated the superiority of CTGAN, reducing the average Euclidean distance between real and synthetic samples by approximately 72% for bladder cancer and by up to 84% for breast cancer compared to a standard GAN. These findings confirm that CTGAN generates high-fidelity synthetic samples that preserve the structural characteristics of real multi-omics data, leading to more reliable class balancing and improved predictive performance. Overall, the proposed framework provides an effective and robust solution for handling class imbalance in multi-omics cancer data and enhances the accuracy of clinically relevant outcome prediction. Full article

In this work, a method for leaching black mass from spent Li batteries using oxalic acid was developed and experimentally verified with the objective of selectively separating lithium and cobalt. Oxalic acid proved to be an efficient and selective leaching agent. Under 1 M C₂H₂O₄, 120 min, L:S = 20, 80 °C and 300 rpm, a lithium yield of 90% was achieved, while cobalt dissolution remained low at 1.57%. Subsequently, cobalt spontaneously precipitated from the leachate within several hours, and the solid phase was fully separated after 24 h. The leachate contained minor amounts of accompanying metals, with dissolution yields of 0.5% Mn, 8% Fe and 1.4% Cu. These impurities were removed from the leachate by controlled pH adjustment using NaOH at ambient temperature and 450 rpm, with complete precipitation at pH 12. This procedure generated a purified lithium-rich leachate, which was converted into lithium oxalate by crystallisation at 105 °C. Subsequent calcination of the resulting solid at 450 °C for 30 min produced Li₂CO₃ with a purity of 91%. Based on the experimental findings, a conceptual technological process for selective lithium leaching using oxalic acid was proposed, demonstrating the potential of this method for sustainable lithium recovery. Full article

For the separation of the close-boiling 1,2-propanediol and ethylene glycol mixture, several process intensification (PI) schemes have been proposed for the two-column configurations. However, no PI technology has yet been investigated for the challenging single-column design operating at atmospheric pressure (SCD). The previously published improvements include the economically modified single-column design (MSCD) as well as high-pressure configurations with (HPDHI) and without (HPD) feed-preheating heat integration. Therefore, this study proposes a partial vapor recompression (SCD-PVR) configuration to intensify this separation using UniSim Design software. Economic and environmental performances were evaluated through total annualized cost (TAC) and CO₂ emissions. When directly compared with the SCD, MSCD, HPD, and HPDHI schemes, the SCD-PVR achieved CO₂ emission reductions of 67.9%, 68.6%, 61.2%, and 56.0%, respectively. Considering a 5-year payback period, SCD-PVR outperformed the SCD and MSCD schemes, decreasing TAC by 9.7% and 11.2%. For a 10-year payback period, the benefits became more significant, with TAC reductions of 31.4%, 32.7%, 17.2%, and 9.3% relative to SCD, MSCD, HPD, and HPDHI. These findings demonstrate that SCD-PVR provides a more energy-efficient, environmentally sustainable, and economically attractive alternative for retrofitting existing plants. Full article

Glycosaminoglycans (GAGs) are highly negatively charged polysaccharides that play essential roles in numerous physiological and pathological processes through their interactions with proteins. These interactions govern cellular signaling, inflammation, coagulation, and recognition. Surface Plasmon Resonance (SPR) has emerged as a key biophysical technique for label-free, real-time characterization of biomolecular interactions, offering insights into binding kinetics, affinity, and specificity. SPR-based approaches to glycosaminoglycan–protein interaction studies offer powerful tools for elucidating the roles of GAGs in a wide range of physiological and pathological processes. In this review, we systematically discuss experimental strategies, data analysis methods, and representative applications of SPR-based glycosaminoglycan–protein interactions. Special attention is given to the challenges associated with GAG heterogeneity and immobilization, as well as recent technological advances that enhance sensitivity and throughput. To our knowledge, this review represents one of the first systematic and up-to-date summaries specifically focused on recent advances in applying SPR to the study of glycosaminoglycan–protein interactions. Full article

Rheology plays an important role in the 3D concrete printing technology, because it directly governs the flowability and shape retention of the material, impacting both the printing process and the final quality of the obtained structure. Local raw materials such as Portland cement, washed sand, and tap water were used for the preparation of 3D-printed concrete mixtures. The solid-state polycarboxylate ether with an anti-foaming agent was used as superplasticizer. The Portland cement was partially replaced (by volume) with a natural zeolite additive in amounts ranging from 0% to 9% in 3D-printed concrete mixtures. A rotational rheometer with coaxial cylinders was used in this research for the determination of rheological characteristics of prepared 3D-printed concrete mixtures. The Herschel–Buckley model was used to approximate experimental flow curves and assess rheological parameters such as yield stress, plastic viscosity, and shear-thinning/thickening index. The additional experiments and calculations, such as water bleeding test and evaluation of the carbon footprint of 3D-printed concrete mixtures, were performed in this work. The replacement of Portland cement with natural zeolite additive positively influenced rheological and stability-related properties of 3D-printed concrete mixtures. Natural zeolite additive consistently reduced water bleeding, enhanced yield stress under increasing shear rates, and lowered plastic viscosity, thereby improving flowability and mixture transportation during the 3D printing process. As the shear-thinning/thickening index remained stable (indicating non-thixotropic behavior in most cases), higher amounts of natural zeolite additive introduced slight thixotropy (especially under decreased shear rates). These changes contributed to better shape retention, layer stability, and the ability to print taller and narrower structures without collapse, making natural zeolite additive suitable for use in the optimized processes of 3D concrete printing. A significant decrease in total carbon footprint (from 3% to 19%) was observed in 3D-printed concrete mixtures with an increase in the mentioned amounts of natural zeolite additive, compared to the mixture without this additive. Full article