Search Results (36,617)

The intensification of wildfires in Portugal has highlighted the urgent need for technical tools capable of supporting more effective risk mitigation decisions. In particular, the lack of explicit criteria for prioritizing the implementation of wildfire mitigation programs has contributed to reactive and fragmented interventions that are often misaligned with actual levels of hazard and exposure. This study proposes a spatially explicit methodology for classifying and ranking villages in wildfire-prone territories under two operational programs: Protection of People, Assets and Fuel Management. The framework was applied to eight municipalities across three Portuguese regions with high wildfire recurrence, using a multi-criteria decision analysis approach (AHP) integrated with geospatial data. Five physical and social variables were considered: critical area, vegetation cover, fire history, slope, and population density. Expert-derived weights were incorporated into two program-specific models. Implementation priority levels were generated using standard deviation classification at both municipal and regional scales. The results reveal marked territorial contrasts and strong intra-municipal variability, particularly in heterogeneous landscapes. A high degree of convergence between the two programs was observed (79–90%), although 10–21% of villages shifted between priority classes. The dual-scale analysis shows how a small number of high-hazard municipalities disproportionately shape the overall priority structure. The proposed framework supports more transparent, consistent, and risk-informed prioritization, strengthening territorial wildfire governance and complementing national mitigation programs such as “Safe Villages” and “Safe People” and “Condominium of Villages”. Full article

Lunar dark mantle deposits (DMDs), formed by explosive volcanic activity on the Moon, are typically composed of glass- and iron-rich pyroclastic materials, with slight variations in color, crystallinity, and TiO₂ concentration by region. This paper proposes a method for identifying DMDs using the YOLOv8 deep learning model, enhanced by the introduction of a multi-scale feature extraction (MSFE) module with an attention mechanism, which improves the model’s ability to detect targets at different scales. First, a DMD dataset was constructed using Lunar Reconnaissance Orbiter (LRO) data, with manual annotations of DMD regions and lunar image slicing to optimize computational efficiency. The YOLOv8 architecture, with the incorporated MSFE module, was then used to improve model accuracy in complex terrain. The experimental results showed that the improved DM-YOLO model achieved a precision (P) of 83.9%, a recall (R) of 83.2%, and a mean average precision (mAP@0.5) of 84.2%, representing increases of 15.2%, 14.4%, and 14.0%, respectively, over those obtained with the original YOLOv8 model. The predicted results were preliminarily verified using FeO abundance data and further confirmed by analysis of M³ spectral absorption features, showing strong consistency with known DMDs in terms of both chemical composition and mineralogical characteristics. Observations showed that DMDs were located primarily in the low- and mid-latitude regions of the Moon, with most deposits found in the lunar highlands. The findings suggest that the DM-YOLO model has significant potential for providing technical support for lunar exploration and resource development, particularly for identifying small-scale features that are difficult to annotate. Full article

Improving energy efficiency in industrial manufacturing remains challenging despite substantial technical potential. This has resulted in a persistent energy efficiency gap, which is increasingly understood as a socio-technical issue driven by not only technology limitations but also organizational and informational barriers. This study investigates how energy waste is targeted in practice through an in-depth single case study of an automotive company. Fifteen energy efficiency measures (EEMs) were analyzed and classified by type of energy waste addressed, digital technologies applied, and organizational knowledge required. The results show that industrial efforts primarily focus on reducing idling energy losses, while fewer measures address more complex forms of energy waste, such as over-processing losses. Digital technologies are mainly applied and rolled out at lower maturity levels, emphasizing energy monitoring and visualization. Further, different types of organizational knowledge are associated with targeting energy waste: technical knowledge dominates isolated interventions, process knowledge supports standardized technology diffusion, and leadership knowledge is required for cross-functional coordination. The findings highlight that bridging the energy efficiency gap requires the alignment of technological solutions with organizational knowledge and routines. This study contributes empirical insights into how manufacturing companies can structure and prioritize energy efficiency efforts and provides a framework to support the implementation of energy efficiency measures in practice. Full article

Rebar binding is a labor-intensive and low-efficiency process in the production of reinforced concrete prefabricated components, in which consistent binding quality is difficult to guarantee. To address the engineering challenges faced by rebar binding robots in complex construction environments—particularly in terms of binding-point recognition accuracy, real-time performance, and manipulator path planning efficiency—this paper presents an integrated method for binding-point recognition, localization, and binding path planning tailored to rebar binding tasks. First, based on the YOLOv8n-pose architecture, a lightweight rebar binding-point recognition and localization model, termed YOLOv8n-pose-Binding, is developed by introducing multi-scale Ghost convolution structures and an adaptive threshold focal loss. The proposed model improves keypoint detection accuracy and real-time performance while effectively reducing computational complexity, making it suitable for deployment on resource-constrained mobile robotic platforms. Second, a dedicated target coordinate system for rebar binding points is constructed to enable accurate pose estimation in the manipulator base frame. Furthermore, considering the non-uniform obstacle distribution in rebar mesh environments and the high-dimensional motion characteristics of robotic manipulators, systematic improvements are introduced to the RRT-Connect framework from the perspectives of sampling strategies, tree expansion, node reconnection, and path pruning, resulting in an improved RRT-Connect path planning algorithm. Simulation and experimental results demonstrate that, while maintaining favorable real-time performance, the proposed method achieves stable improvements in recognition accuracy and inference efficiency compared with the baseline YOLOv8n-pose model. In addition, the improved RRT-Connect algorithm exhibits superior engineering performance in terms of path planning efficiency and path quality, providing a deployable technical solution for automated rebar binding operations. Full article

Introduction: HPV vaccination rates for adolescents in the United States are below recommended levels. The Adolescent Vaccination Program (AVP) guides pediatric clinics on how to implement evidence-based strategies to increase HPV vaccination rates. These strategies comprise the adoption of (1) immunization champions, (2) provider assessment and feedback, (3) continuing education, and (4) prompts, (5) parent reminders, and (6) parent education. The AVP systems-based intervention has demonstrated increased HPV vaccination rates in large urban pediatric clinic networks. The purpose of this study was to assess the feasibility of using an online decision support tool, the AVP Implementation Tool (AVP-IT), to implement AVP strategies in safety-net clinics to improve healthcare for the medically underserved in Texas. Methods: AVP immunization clinic staff champions in four urban safety-net clinics completed tailored Action Plans within the AVP-IT to guide strategy implementation, received webinar training from the research team commensurate to each AVP strategy, and participated in monthly monitoring calls with AVP-IT project staff over a 33-month period from 2022 to 2024. Results: All clinics made progress toward full implementation of AVP strategies. Interrupted time series (ITS) trend analysis demonstrated that AVP-IT implementation was associated with an immediate boost in HPV vaccine initiation rates (p < 0.001) and that long-term trends (ITS slopes) were significant for HPV, MCV4, and Tdap vaccines despite low post-COVID-19 pandemic rates (p < 0.001). Vaccination rates using raw data (mean differences) were not longitudinally significant except for older youth aged 13–17 years. Conclusion: The AVP-IT promises accessible and practical decision support to implement strategies to increase HPV vaccination rates in safety-net clinics. Scale-up in these clinics will require leadership support, technical assistance, and EHR optimization. Full article

Natural and human-made disasters threaten cities increasingly, thus requiring a combination of disaster risk reduction and sustainable development strategies. Although vulnerability assessment methods and urban sustainability policies have improved significantly, these two fields remain separate, leading to fragmented policies that may work against resilience objectives. This paper provides an overview by conducting a systematic review of 87 peer-reviewed studies published between 2000 and 2024 that describe and analyze the intersection of disaster prevention policies and sustainability practices in urban planning. Thematic analysis was employed, and five major themes were revealed: policy implementation frameworks, climate adaptation strategies, preparedness mechanisms, vulnerability assessment approaches, and sustainability evaluation systems. The findings reveal a critical disconnect: on the one hand, vulnerability assessments highlight the structural–technical aspect and, at the same time, ignore the sustainability indicators (resource efficiency, social equity, and ecosystem services), while on the other hand, sustainability frameworks deliberately shut out disaster risk awareness from the core evaluation criteria. This methodological separation produces policy conflicts where disaster interventions may compromise environmental goals, and sustainability initiatives may increase hazard vulnerability. This review concludes that resilient cities require assessment methodologies synthesizing disaster risk and sustainability dimensions. A novel conceptual integration framework is suggested that combines hazard-exposure-vulnerability analysis with environmental–social–economic sustainability pillars, thus laying the groundwork for future operational tools. This joint viewpoint accepts that hazards mainly affect development that is not sustainability-oriented, while sustainable systems through adaptive design and equitable resource distribution inherently lower the vulnerability. Full article

The adoption of Lean Construction has become established as an effective strategy for improving efficiency and reducing waste in construction projects; however, its implementation faces numerous challenges. The objective of this study is to analyze, through a systematic literature review, the critical factors that influence the implementation of Lean Construction, based on the analysis of 52 articles published between 2015 and 2025. The search was conducted in the Scopus database, and the selection of studies followed the PRISMA methodology. The results are organized into six key aspects that influence the execution of construction projects, grouped into three main categories: human, which includes managerial (42.3%) and educational (21.2%) factors; technical, which encompasses technological (23.1%) and safety (5.8%) factors; and management-related factors, comprised of financial (1.9%) and logistical (5.8%) factors. The evidence shows a greater relevance of human-related factors, followed by associated technical factors and, to a lesser extent, management-related factors. Taken together, these results highlight the need to address these factors through integrated strategies, training programs, and committed leadership to achieve a successful and sustainable implementation of Lean Construction. Full article

The new European Regulation (EU) 2025/40 includes provisions on modern packaging and packaging waste. It defines the use of image QR codes on packaging (items 71 and 161) and in personal documents, making line barcodes a thing of the past. The definition of a QR code is precisely specified in ISO/IEC 18004:2024. However, their implementation in printing systems is not specified and remains an important factor for their future application. Digital foil printing is a completely new hybrid printing process for applying information to highly precise applications such as QR codes, security printing, and packaging printing. The technique is characterized by a combination of two printing techniques: drop-on-demand UV inkjet followed by thermal transfer of black foil. Using a matte-coated printing substrate (Garda Matt, 300 g/m²), Konica Minolta KM1024 LHE Inkjet head settings, and a transfer temperature of 100 °C, the size of the square printing elements in QR codes plays a decisive role in the quality of the decoded information. The aim of this work is to investigate the possibility of realizing the basic elements of the QR code image (the profile of square elements and the success of realizing a precisely defined surface) with a variation in the thickness of the UV varnish coating (7, 14 and 21 µm), realized using the MGI JETvarnish 3DS digital machine. The most commonly used rectangular elements with a surface area of 0.01 cm² were tested: 0.06 cm², 0.25 cm², 1 cm², 4 cm², and 16 cm². The results showed that the imprint quality is uneven for the smallest elements (square elements with base lengths of 0.1 cm and 0.25 cm). The effect is especially visible with a minimum UV varnish application of 7 μm (1 drop). By increasing the amount of UV varnish and the application thickness to 14 μm (2 drops) and 21 μm (3 drops), respectively, a significantly more stable, even reproduction of the achromatic image is achieved. The highest technical precision was achieved with a UV varnish thickness of 21 μm. Full article

To establish a rapid, sensitive, and reproducible method for evaluating the immunogenic performance of Tembusu virus (TMUV) vaccines, we developed and optimized a blocking enzyme-linked immunosorbent assay (bELISA) using the TMUV envelope (E) protein as the coating antigen. By systematically screening the coating antigen concentration, mAb dilution, serum dilution, and chromogenic reaction time, we determined the optimal reaction conditions for this assay. The results showed that bELISA exhibited high specificity, yielding positive reactions only with TMUV-positive sera and no cross-reactivity with sera against other common duck viruses; the cutoff value for positivity was 48.89%, and the lowest detectable serum dilution was 1:10. Neutralization assays confirmed that the TMUV E-specific mAb significantly inhibited viral replication, supporting the functional relevance and reliability of the established bELISA. In a comparative investigation, this assay was used to assess five TMUV vaccines, including both inactivated and attenuated variants, in Cherry Valley ducks. The DF2 inactivated vaccine was found to elicit the highest antibody levels and blocking rates. This was followed by the WF100 attenuated vaccine, which also demonstrated a strong immune response. The TC2B inactivated vaccine, although effective, showed a comparatively lower response, whereas the FX2010-180P strain and mosquito cell-derived WF100 attenuated vaccine showed weaker immunogenicity. Neutralization assays further confirmed that the TMUV E-specific mAb significantly inhibited viral replication, supporting the functional relevance and reliability of the established bELISA. In summary, the bELISA described here demonstrates high specificity, sensitivity, and reproducibility and is suitable for evaluating the immune efficacy of different TMUV vaccines, providing a reliable technical platform for vaccine immunology studies and optimization of immunization strategies. Full article

Background: Traditional high-speed mechanical osteotomes cause substantial thermal and mechanical trauma, impairing bone healing. Erbium-doped yttrium-aluminum-garnet (Er:YAG) lasers, with water-mediated non-contact ablation, offer precise osteotomy potential with minimal collateral damage. This study demonstrated the feasibility of Er:YAG laser use for complex osteotomies and elucidated its multi-scale biological impacts on bone. Methods: A custom Er:YAG laser performed Z/arc-shaped osteotomies on fresh ovine bone (oscillating saw as control); paired rat tibial osteotomies; and compared laser vs. saw resection. Osteotomy surfaces were characterized by SEM/micro-CT; histological staining quantified thermal/mechanical damage. Bone marrow-derived mesenchymal stem cell (BMSC) adhesion, viability, and infiltration on cut surfaces were evaluated via LSCM. Result: In the ex vivo ovine model, the Er:YAG laser enabled precise execution of complex osteotomies (Z-shaped and arc-shaped), producing significantly narrower gaps than the oscillating saw (1.14 mm vs. 2.70 mm, p < 0.001) with high geometric fidelity and smooth surfaces free of burrs, micro-cracks, or debris. In the in vivo rat model, laser ablation simultaneously minimized both thermal and mechanical damage at the osteotomy interface: it reduced the thermal damage depth (154 vs. 592 µm, p < 0.001) and empty lacunae rate (16.8% vs. 41.8%, p < 0.001) while completely avoiding the mechanical damage zone (297 µm) induced by sawing. Furthermore, the laser-ablated surface established a highly bioactive interface, which significantly enhanced the adhesion (606 vs. 389 cells), viability (86.9% vs. 46.6%), and infiltration depth (196 vs. 75 µm) of bone marrow-derived mesenchymal stem cells (all p < 0.001). Conclusions: In conclusion, this proof-of-concept study demonstrates that the Er:YAG laser has the potential to enable precise bone resection while preserving microstructure. By establishing a pro-regenerative microenvironment, this technology shows promise as a biologically favorable alternative to conventional sawing, although further technical refinement and long-term validation are essential for its clinical translation. Full article

To realize the efficient recovery and utilization of rare earth resources, this study systematically investigates the preparation process of recycled-rare earth polishing powder using rare earth intermediates (purified from waste polishing slag) as raw materials. This work focuses on two core stages: precursor synthesis and high-temperature calcination. During the precursor preparation stage, the particle size of the precursor was controlled by optimizing the ball milling process (with a ball milling time of 1 h, a ball-to-material ratio of 3:1, and 2 mm zirconia balls), yielding an optimal D50 of 6.5 μm. The Ce/La ratio was modulated by adding cerium carbonate (the conventional ratio is 65:35, which can be adjusted as needed within the range of 65:35–80:20). Furthermore, fluorine (3–7%) and a small amount of praseodymium were incorporated to enhance the polishing performance. In the high-temperature roasting stage, single-factor tests were conducted to determine the optimal staged heating rates (below 400 °C: 5 °C/min; 400–700 °C: 3 °C/min; above 700 °C: 1.5 °C/min) and a holding time of 4 h at 950 °C. Under these optimized conditions, the resulting polishing powder exhibits a material removal rate exceeding 350 mg·h⁻¹ and maintains stable performance over three consecutive polishing cycles. This study demonstrates that by regulating the chemical composition and physical parameters of the precursor, as well as optimizing the high-temperature roasting process, recycled-rare earth polishing powders with tailored performance characteristics can be custom-manufactured. This approach balances the polishing ability and production cost, thus providing technical support for the industrial production of recycled-rare earth polishing powder. Full article

The accuracy of forest canopy height estimation is crucial for forest resource management and ecosystem carbon sequestration. However, existing approaches often face limitations in effectively integrating multisource remote sensing data, feature representation, and model learning strategies. To enhance the prediction performance of the model in complex terrain and multisource data environments, this study comprehensively used ICESat-2/ATLAS photon point clouds, Sentinel-2/MSI multispectral imagery, and SRTM-DEM to construct a remote sensing-driven multisource feature system, which eliminated redundant interference using permutation feature importance analysis. Additionally, a self-attention (SA) mechanism was introduced to strengthen high-dimensional feature representation. Three heterogeneous models, incorporating deep neural network (DNN), extreme gradient boosting (XGBoost), and residual network (ResNet), were independently applied for forest canopy height estimation and were further used as base learners, with a random forest as the meta-learner, and an SA-Blending heterogeneous ensemble model that combines a blending technique with an SA mechanism was proposed to enhance the accuracy of forest canopy height estimation. To evaluate the SA optimization strategy and the role of multisource fusion, this study used the original features, SA-optimized features, and multisource fusion features (i.e., the concatenation and fusion of original features and self-attention mechanism features) as inputs to comprehensively compare the performance of each single model and the integrated model. The results show that: (1) The self-attention mechanism significantly improves the prediction performance of heterogeneous models. Compared with original features inputs, the R² of DNN (SA-Only) and XGBoost (SA-Only) increased to 0.706 and 0.708, respectively, and the RMSE decreased to 1.691 m and 1.613 m. Although the R² for ResNet (SA-Only) decreased slightly to 0.699 and the RMSE increased to 1.712 m, the overall impact was not significant. (2) Under the condition of multisource fusion feature input, DNN+SA, XGBoost+SA, and ResNet+SA all demonstrated higher fitting accuracy and stability, verifying the enhancing effect of the SA mechanism on the association expression of multisource information. (3) The SA-Blending model achieved the best overall performance, with R² of 0.766 and RMSE of 1.510 m. It outperformed individual models and the SA-optimized model in terms of overall accuracy, stability, and robustness. The results can provide technical support for high-precision forest canopy height mapping and are of great significance for ecological monitoring applications. Full article

Calcium carbide slag and silica fume was used as a cement replacement material, combined with excavated soil and EPS (expanded polystyrene) particles, to develop a new green and low-carbon lightweight fluid solidified soil (LFSS). Focusing on the performance regulation of LFSS, this study adopted the paste volume ratio (PV, defined as the volume ratio of paste to total mixture) and the water–binder ratio (w/b) to systematically construct a mix ratio design system and proposed EPS particle interface modification and shell formation technology to improve the weak interface bonding between EPS and the matrix. Firstly, based on the paste volume method, the effects of PV and w/b on the flowability and strength of LFSS were analyzed, and a linear correlation model between the water–solid volume ratio and flowability, as well as a quadratic function prediction model for 28-day strength, was established. Secondly, the “core–shell structure” of EPS particles was constructed by combining EVA (ethylene-vinyl acetate) modification with the coating of calcium carbide slag–silica fume paste. Considering the influence of the coating method, w/b, and material mass ratio on interface bonding comprehensively, the optimal process parameters were determined to achieve the interface reinforcement of EPS particle. The results showed that the water–solid volume ratio was significantly linearly correlated with the flowability of LFSS. PV and w/b respectively controlled the framework formation and pore structure evolution of LFSS, with optimal overall performance at PV = 0.55 and w/b = 2.5. The modification shell formation significantly reduced the shell loss rate of EPS particles and increased the 28-day compressive strength of LFSS by 21.7%. SEM (scanning electron microscope) and EDS (energy-dispersive spectroscopy) analysis further revealed that the shell-formation technique promoted the densification of the interface transition zone, enhanced the deposition of hydration products, and strengthened the synergistic effect of Na and Ca elements, thereby significantly improving interface bonding and overall structural stability. This study established a “mix ratio optimization-modification and shell formation” dual-regulation mechanism, providing an effective technical approach and theoretical basis for the engineering application of calcium carbide slag–silica fume-based LFSS. Full article

The increasing functional complexity and high-density integration of integrated circuits (ICs) present formidable routing challenges for printed circuit boards. Specifically for components utilizing high-density Ball Grid Array and other Grid Pin Array (GPA) packages, achieving efficient and reliable ordered escape routing is critical. This routing must strictly satisfy non-crossing and channel capacity constraints, which often become the bottleneck determining design success. Traditional manual or heuristic methods are increasingly inadequate for meeting the complexity and optimization demands of modern high-density designs. To address this challenge, this study proposes an innovative, automated routing strategy that leverages the robust search and optimization capabilities of the genetic algorithm (GA). We formulate the ordered escape routing problem on BGA/GPA as a rigorous combinatorial optimization problem. Through the GA mechanism, the core research objective is to explore the vast solution space and achieve the minimization of the total routing cost. This research establishes an automated routing framework capable of providing cost-effective escape routing solutions for high-density BGA, while strictly satisfying the non-crossing and single-capacity constraints. The proposed methodology not only significantly enhances routing efficiency and success rates but also provides essential technical support for the design and manufacturing processes of complex electronic products. For instance, in the simulation case of Case 7 (a 25 × 26 array with 60 active pins), this method achieved a routing compression ratio of 0.8230, equivalent to a 17.70% reduction in the total routing length, fully demonstrating the superior performance of the proposed algorithm in optimizing the routing cost. Full article

Milk is a primary source of vital nutrients and bioactive components fundamental to the growth and development of both newborn animals and humans. Produced by economically significant livestock species (including cattle, buffaloes, goats, sheep and camels), milk is a complex matrix rich in caseins, vitamins, fats, and proteins. Beyond its classical nutritional profile, milk serves as a pivotal vehicle for milk-derived extracellular vesicles (mEVs). These specialized food-derived EVs (fEVs) exert pleiotropic effects that resonate with the One Health paradigm, linking animal well-being and human nutrition to broader ecosystem stability. mEVs offer unique advantages, such as high biocompatibility and gastrointestinal stability, also rendering them potential therapeutic tools as drug delivery systems. However, challenges remain regarding the standardization of mEVs and the variability of their molecular cargo. This review provides a comprehensive comparative analysis of mEVs across a diverse taxonomic range, including bovines, water buffaloes, yaks, camels, goats, pigs, horses, donkeys, and humans, highlighting their distinct functional signatures. Indeed, a critical issue in mEV research is the isolation process: recommendations to minimize contamination from milk fat globules and casein micelles (which can cover EV signals) are given. Finally, current detection methods and instrumentation, with a specific focus on advancing flow cytometry (FC) approaches are discussed. Key insights include the use of conventional FC (with fluorescence triggering, the necessity of rigorous controls and calibration, and the utility of bead-based assays to overcome resolution limits) and imaging flow cytometry (IFC). In both technical approaches, the application of different EV generic fluorescent markers and the strategic selection of tetraspanins (i.e., CD9, CD63, CD81), is mandatory: we emphasize that selecting the correct antibody clones and accounting for inter-species cross-reactivity are essential steps for ensuring the accuracy and reproducibility of mEV research across mammalian species. Full article