Search Results (67)

This study investigates the use of the fine fraction of Brazilian residual kaolin, a material with no pozzolanic activity according to the modified Chapelle test, as a partial cement replacement in rendering mortars. The kaolin was classified into three granulometric fractions (coarse: 150–300 µm, intermediate: 75–150 µm, and fine: <75 µm) and incorporated at two filler contents (10% and 20% by weight). Mineralogical and chemical analyses revealed that the fine fractions contained higher proportions of kaolinite and accessory oxides, while medium and coarse fractions were dominated by quartz. Intensity ratios from XRD confirmed greater structural disorder in the fine fraction, which was associated with higher water demand but also improved particle packing and pore refinement. Fresh state tests showed that mortars with fine kaolin maintained higher density and exhibited moderate increases in air content, whereas medium and coarse fractions promoted greater entrainment. In the hardened state, fine kaolin reduced water absorption by immersion and capillary rise, while medium and coarse fractions led to higher porosity. Mechanical tests confirmed these trends: although compressive and flexural strengths decreased with increasing substitution, mortars containing the fine kaolin fraction consistently exhibited more moderate strength losses than those with medium or coarse fractions, reflecting their enhanced packing efficiency and pore refinement. Tensile bond strength results further highlighted the positive contribution of the kaolin additions, as the mixtures with 10% coarse kaolin and 20% fine kaolin achieved adhesion values only about 7% and 4% lower, respectively, than the control mortar after 28 days. All mixtures surpassed the performance requirements of NBR 13281, demonstrating that the incorporation of residual kaolin—even at higher substitution levels—does not compromise adhesion and remains compatible with favorable cohesive failure modes in the mortar layer. Despite the lack of pozzolanic activity, residual kaolin was used due to its filler effect and capacity to enhance particle packing and pore refinement in rendering mortars. A life cycle assessment indicated that the partial substitution of cement with residual kaolin effectively reduces the environmental impacts of mortar production, particularly the global warming potential, when the residue is modeled as a by-product with a negligible environmental burden. This highlights the critical role of methodological choices in assessing the sustainability of industrial waste utilization. Full article

In this paper, we propose a multi-layered hierarchical block tracking (HBT) system for continuous real-time sensing and efficient network management of highly mobile nomadic infrastructure. In order to solve the limitations of the existing high-resolution satellite direct connection method in the fast tracking and management of mobile infrastructure due to the high processing delay and large data processing burden, we introduce event-based data abstraction and Infra Map management in a multi-layered network consisting of a Low Earth Orbit satellite (LEO), high-altitude platform (HAP), and low-altitude platform (LAP). Through this, unnecessary data transmission is minimized and processing speed and Surveillance Resolution (SR) are improved. Experimental results show that the HBT structure achieves the improved SR at low-speed conditions, maintaining high tracking stability even under dynamic movement scenarios, while reducing processing delay when compared to the existing LEO–ground direct communication. As a result, we verify that the HBT structure shows lower processing delay and high tracking stability when compared to the existing LEO–ground direct communication. Full article

Modernizing municipal roads requires rehabilitation strategies that ensure adequate structural performance while reducing environmental and economic burdens. Although cold in-place recycling with foamed bitumen (CIR-FB) has been widely investigated, integrated assessments combining mechanistic–empirical modeling with LCA and LCCA remain limited—particularly for municipal roads in Central and Eastern Europe, where reclaimed asphalt pavement (RAP) quality, climatic conditions and budget constraints differ from commonly studied regions. This study compares two reconstruction variants for a 1 km road section: a conventional design using virgin materials (V₁-N) and a recycling-based alternative (V2-Rc) incorporating RAP from the existing wearing and binder layers and reclaimed aggregate (RA) from the existing base. CIR-FB mixture testing (stiffness ≈ 5.25 GPa; foamed bitumen = 2.5%, cement = 2.0%) was integrated into mechanistic–empirical fatigue analysis, material-flow quantification, LCA and LCCA. The V₂-Rc variant achieved a 3–21-fold increase in fatigue life compared to V₁-N at equal thickness. Material demand decreased by approximately 27%, demolition waste by approximately 39%, and approximately 92% of the existing pavement was reused in situ. Transport work was reduced five-fold (veh-km) and more than six-fold (t-km). LCA showed a 15.9% reduction in CO₂-eq emissions, while LCCA indicated approximately 19% lower construction cost, with advantages remaining robust under ±20% sensitivity. The results demonstrate that CIR-FB, when supported by proper RAP/RA characterization, can substantially improve structural, environmental and economic performance in municipal road rehabilitation. Full article

Cymbal piezoelectric energy harvesters offer an effective platform for converting mechanical vibrations into electrical energy due to their ability to exploit both longitudinal (d₃₃) and transverse (d₃₁) piezoelectric coefficients. However, the design of flexible cymbal structures that ensure efficient stress transfer to polymer-based piezoelectric materials remains insufficiently explored. In this study, a bridge-like cymbal harvester incorporating polyvinylidene fluoride (PVDF) films as the active layer was designed, fabricated, and experimentally investigated. To support the design process and reduce the computational burden associated with evaluating multiple geometric configurations, we developed a novel machine learning methodology that integrates singular value decomposition (SVD) with metamodeling. This framework provides rapid predictions of resonance behavior and electrical response from key design parameters. The findings demonstrate the feasibility of PVDF-based cymbal harvesters for flexible energy harvesting applications and establish an efficient data-driven approach for guiding future design optimization. Full article

Between 51% and 72% of a bituminous roofing membrane used for structural waterproofing consists of organic material, predominantly bitumen—a derivative of crude oil refining—highlighting the strong dependence of this product on fossil resources. Considering that several tonnes of these membranes must be replaced every 30 to 50 years, substantial potential exists for emission reduction through the establishment of circular material systems. This study investigates this potential by analysing 26 Environmental Product Declarations (EPDs) and life cycle datasets from across Europe covering the period from 2007 to 2023. To ensure comparability, all data were normalised to a declared unit of 1 kg of roofing membrane. The reinforcement layers were categorised into glass and polyester & glass composites, and their differences were examined using Welch’s t-tests. Correlative analyses and linear as well as multiple regression models were then applied to explore relationships between environmental indicators and the shares of organic and mineral mass fractions. The findings reveal that renewable energy sources, although currently representing only a small share of total production energy, provide a major lever for reducing nearly all environmental impact categories. The type of reinforcement layer was also found to influence the demand for fossil resources, both materially and energetically. For most environmental indicators, only multiple regression models can explain at least 30% of the variance based on the proportions of organic and mineral inputs. Overall, the study underscores the crucial importance of high-quality, transparently documented product data for accurately assessing the sustainability of building products. It further demonstrates that substituting fossil energy carriers with renewable sources and optimising material efficiency can substantially reduce environmental burdens, provided that methodological consistency and clarity of indicator definitions are maintained. Full article

The increasing market demand for high-power and high-frequency applications necessitates the development of highly reliable Gallium Nitride (GaN) High-Electron-Mobility Transistors (HEMTs). While GaN offers superior performance and efficiency over traditional silicon, gate-related defects pose a significant reliability challenge, often leading to premature device failure under stress. Traditional High-Temperature Gate Bias (HTGB) testing is effective but time-consuming and costly, particularly when defects are only identified post-packaging. This study focuses on developing an effective wafer-level screening methodology to mitigate the financial burden and reputational risk associated with late-stage defect discovery. Failure analysis of an HTGB premature failure revealed a gate metal deposition defect characterized by identical elemental composition to the bulk metal, suggesting a small-volume structural anomaly. Crucially, a comparative analysis showed that Forward Gate Current (I_GON) is an insensitive screening metric due to high inherent gate leakage through the passivation layer. In contrast, the Reverse Gate Current (I_GOFF) exhibited sensitivity, particularly under the tensile stress induced by package molding, which is attributed to the piezoelectric effect altering the depletion region width beneath the p-GaN gate. Based on this observation, a multi-pulse I_DSS test was developed as a wafer-level screen. This method successfully amplified the subtle electrical field perturbations caused by the gate defect. After screening 231 dies using the new methodology, zero failures were recorded after 1000 h of HTGB stress, a significant improvement over the initial failure rate of 0.43% (1 out of 231). This work demonstrates that early, sensitive wafer-level screening of gate defects is indispensable for optimizing manufacturing yield and enhancing long-term device reliability. Full article

Antenna structure design constitutes a computationally expensive optimization problem due to the requirement for full-wave electromagnetic (EM) simulations. Surrogate-assisted evolutionary algorithms offer a promising approach for addressing such challenges. However, several challenges remain in solving expensive, highly constrained antenna design problems. This paper introduces a surrogate-assisted dynamic constrained multi-objective evolutionary algorithm framework to tackle expensive and highly constrained antenna design optimization tasks. A multi-layer perceptron (MLP) is employed as the surrogate model to approximate EM evaluations and alleviate the computational burden, while a dynamic scale-constrained boundary strategy is implemented to handle highly constraints. The effectiveness of the proposed method is validated on a set of constrained benchmark problems and two antenna design cases. Full article

Cracks in concrete dam tunnels compromise structural safety, watertightness, and durability, while conventional repair materials such as epoxy and cement impose environmental burdens. This study investigates biomineralization methods, namely Microbially Induced Calcium Carbonate Precipitation (MICP) and Enzyme-Induced Carbonate Precipitation (EICP), for repairing fine cracks in a large hydropower dam tunnel. Laboratory tests and field applications were conducted by injecting urea–calcium solutions with Sporosarcina pasteurii for MICP and soybean-derived urease for EICP, applied twice daily over three days. Both techniques achieved effective sealing, with precipitation efficiencies of 93.75% for MICP and 84.17% for EICP. XRD analysis revealed that MICP produced a mixture of vaterite and calcite, reflecting biologically influenced crystallization, whereas EICP yielded predominantly calcite, the thermodynamically stable phase. SEM confirmed that MICP generated irregular layered clusters shaped by microbial activity, while EICP formed smoother spherical and more uniform deposits under enzyme-driven conditions. The results demonstrate that MICP provides higher efficiency and localized nucleation control, while EICP offers faster kinetics and more uniform deposition. Both methods present eco-friendly and field-applicable alternatives to conventional repair, combining technical performance with environmental sustainability for hydraulic infrastructure maintenance. Full article

The accurate detection of tea buds in outdoor environments is crucial for the intelligent management of modern tea plantations. However, this task remains challenging due to the small size of tea buds and the limited computational capabilities of the edge devices commonly used in the field. Existing object detection models are typically burdened by high computational costs and parameter loads while often delivering suboptimal accuracy, thus limiting their practical deployment. To address these challenges, we propose TeaBudNet, a lightweight and robust detection framework tailored for small tea bud identification under outdoor conditions. Central to our approach is the introduction of Weight-FPN, an enhanced variant of the BiFPN designed to preserve fine-grained spatial information, thereby improving detection sensitivity to small targets. Additionally, we incorporate a novel P2 detection layer that integrates high-resolution shallow features, enhancing the network’s ability to capture detailed contour information critical for precise localization. To further optimize efficiency, we present a Group–Taylor pruning strategy, which leverages Taylor expansion to perform structured, non-global pruning. This strategy ensures a consistent layerwise evaluation while significantly reducing computational overhead. Extensive experiments on a self-built multi-category tea dataset demonstrate that TeaBudNet surpasses state-of-the-art models, achieving +5.0% gains in AP@50 while reducing parameters and computational cost by 50% and 3%, respectively. The framework has been successfully deployed on Huawei Atlas 200I DKA2 developer kits in real-world tea plantation settings, underscoring its practical value and scalability for accurate outdoor tea bud detection. Full article

This paper presents a novel data-driven approach to structural health monitoring (SHM) that uses Echo State Network (ESN) regression for continuous damage assessment. In contrast to traditional classification methods that demand extensive labeled data on damaged states, our approach utilizes an ESN, a powerful recurrent neural network, to directly predict a continuous damage metric from sensor data. This regression-based methodology offers two key advantages relevant to data science applications in SHM: (1) Reduced Training Data Dependency: The ESN achieves high accuracy even with limited data on damaged structures, significantly alleviating the data acquisition burden compared to classification-based AI/ML techniques. (2) Enhanced Noise Resilience: The inherent reservoir computing property of ESNs, characterized by a fixed, high-dimensional recurrent layer, makes them more tolerant of sensor noise and environmental variations compared to classification methods, leading to more reliable and robust SHM predictions from noisy data. A comprehensive evaluation demonstrates the effectiveness of the proposed ESN in identifying structural damage, highlighting its potential for practical application in data-driven SHM systems. Full article

Emergency department (ED) triage is the cornerstone of ED operations. Many different triage systems have been proposed and implemented globally. To date, an ideal triage system has not yet been identified. As the burden on EDs rises, with overcrowding being recognized as a universal problem, ED triage needs to be restructured to address this reality. Extensive and critical literature research over the years has identified the strengths and weaknesses of current ED triage implementations. A novel multi-layer triage system was introduced and implemented in Greek Eds, combining the strengths of various triage and early warning systems and scores to minimize under-triage and the adverse downstream effects it creates on patient outcomes. Acknowledging that no triage system can be universally adapted in different settings, the structural concepts of this triage system address most of the triage problems currently reported in the literature. Full article

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. Methods: In this study, two novel deep learning models based on fractal architectures are proposed for the automatic detection and segmentation of MS lesions in the CSC by improving the convolutional and connection structures used in the layers of the U-Net architecture. In our previous study, we introduced the FractalSpiNet architecture by incorporating fractal convolutional block structures into the U-Net framework to develop a deeper network for segmenting MS lesions in the CPC. In this study, to improve the detection of smaller structures and finer details in the images, an attention mechanism is integrated into the FractalSpiNet architecture, resulting in the Att-FractalSpiNet model. In addition, in the second hybrid model, a fractal convolutional block is incorporated into the skip connection structure of the U-Net architecture, resulting in the development of the Con-FractalU-Net model. Results: Experimental studies were conducted using U-Net, FractalSpiNet, Con-FractalU-Net, and Att-FractalSpiNet architectures to detect the CSC region and the MS lesions within its boundaries. In segmenting the CSC region, the proposed Con-FractalU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) score of 98.89%. Similarly, in detecting MS lesions within the CSC region, the Con-FractalU-Net model again achieved the best performance with a DSC score of 91.48%. Conclusions: For segmentation of the CSC region and detection of MS lesions, the proposed fractal-based Con-FractalU-Net and Att-FractalSpiNet architectures achieved higher scores than the baseline U-Net architecture, particularly in segmenting small and complex structures. Full article

Overcoming the limitations of powder-based additive manufacturing processes is a crucial aspect for the manufacturing of patient-specific sophisticated implants with tailored properties. Within this work, a novel manufacturing process for the fabrication of polymer-based implants is proposed. This manufacturing process is inspired by the laminated object manufacturing technology and is based on using thin films as raw material, which are processed using an ultrafast laser source. Utilizing thin films as a starting material helps to avoid powder contamination during additive manufacturing, thus supporting the generation of internal cavities that can be filled with secondary phases. Additionally, the use of medical materials mitigates the burden of a later certification of potential implants. Furthermore, the ultrafast laser supports the generation of highly resolved structures smaller than the average layer thickness (from 50 to 100 µm) through material ablation. These structures can be helpful to obtain progressive part properties or a targeted stress flow, as well as a specified release of secondary phases (e.g., hydrogels) upon load. Within this work, first investigations on the joining, cutting, and structuring of thin polymer films with layer thickness of between 50 and 100 µm using a ps-pulsed laser are reported. It is shown that thin film sizes of around 50 µm could be structured, joined, and cut successfully using ultrafast lasers emitting in the NIR spectral range. Full article

The real-time detection of facial skin problems is crucial for improving dermatological health, yet its practical implementation remains challenging. Early detection and timely intervention can significantly enhance skin health while reducing the financial burden associated with traditional dermatological treatments. This paper introduces EM-YOLO, an advanced deep learning framework designed for embedded and distributed environments, leveraging improvements in YOLO models (versions 5, 7, and 8) for high-performance, real-time skin condition detection. The proposed architecture incorporates custom layers, including Squeeze-and-Excitation Block (SEB), Depthwise Separable Convolution (DWC), and Residual Dropout Block (RDB), to optimize feature extraction, enhance model robustness, and improve computational efficiency for deployment in resource-constrained settings. The proposed EM-YOLO model architecture clearly delineates the role of each architectural component, including preprocessing, detection, and postprocessing phases, ensuring a structured and modular representation of the detection pipeline. Extensive experiments demonstrate that EM-YOLO significantly outperforms traditional YOLO models in detecting facial skin conditions such as acne, dark circles, enlarged pores, and wrinkles. The proposed model achieves a precision of 82.30%, recall of 71.50%, F1-score of 76.40%, and mAP@0.5 of 68.80%, which are 23.52%, 32.7%, 29.34%, and 24.68% higher than standard YOLOv8, respectively. Furthermore, the enhanced YOLOv8 custom layers significantly improve system efficiency, achieving a request rate of 15 Req/s with an end-to-end latency of 0.315 s and an average processing latency of 0.021 s, demonstrating 51.61% faster inference and 200% improved throughput compared to traditional SCAS systems. These results highlight EM-YOLO’s superior precision, robustness, and efficiency, making it a highly effective solution for real-time dermatological detection tasks in embedded and distributed computing environments. Full article

An improved YOLOv8n seamless fabric defect detection model is proposed to solve the current issues in seamless fabric defects in factories in this paper. The improvement in this paper first introduces the SPPF_LSKA module, which not only optimizes the extraction of multi-scale features but also enhances the adaptability of the model in detecting defects of different sizes by improving the feature fusion mechanism, enabling efficient recognition of both large-sized and small-sized defects. Secondly, the CARAFE upsampling method is used to adaptively learn the relationship between pixels, which not only reduces information loss but also improves the reconstruction quality of feature maps, which is crucial for capturing complex textures and subtle defects of seamless fabrics. In addition, adding a small object detection layer particularly improves the detection accuracy of the model for small-sized defects, making it no longer limited to traditional models when dealing with high-density fabrics or small defects. Finally, integrating OREPA technology significantly reduces computational complexity, reduces redundant computing burden, and accelerates the training process by optimizing the model structure. The experimental results show that the precision, recall, and mAP@0.5 of the model on the seamless fabric defect dataset have improved by 7.3%, 8.5%, and 5.1%, respectively, compared to the baseline model YOLOv8n. Future research aims to explore the application of the model further in practical scenarios and complete the actual deployment of the seamless fabric defect detection system. Full article