Search Results (748)

Accurate assessment of avian community structure and bird-strike risk within airport ecosystems is vital for balancing aviation safety with biodiversity conservation. From October 2019 to July 2020, we conducted systematic bird surveys at Lincang Boshang Airport (Yunnan, China) and its surrounding area. By integrating taxonomic, functional, and phylogenetic diversity analyses, we examined spatial–temporal patterns of bird diversity and characterized bird-strike risk. In total, 4859 individuals of 148 species were recorded, representing 51 families and 15 orders. The avifauna was dominated by broadly distributed Oriental–Palearctic species, reflecting the pronounced biogeographic transition of southwestern Yunnan. Functional diversity (FD) and phylogenetic diversity (PD) differed significantly among habitats: wetlands exhibited the highest FD and PD, indicating strong functional and lineage overdispersion driven by high environmental heterogeneity, whereas farmland showed the lowest FD and PD, consistent with stronger environmental filtering. Seasonal dynamics also shaped community structure, with the highest individual abundance in winter and the lowest species richness in spring. Standardized effect sizes (SES) revealed an overall tendency toward functional and phylogenetic clustering (SES < 0), most pronounced in forest and urban communities, while wetland assemblages consistently showed greater overdispersion across seasons. Risk evaluation indicated that low-risk species comprised 76.35% of the fauna, whereas high and very-high risk species accounted for only 3.38%, mainly large raptors (Accipitriformes) and pelicans/herons (Pelecaniformes). Integrating community patterns with risk distribution, we propose zone-specific management: remove standing water and tall grass in wetlands and farmland; optimize vegetation structure along forest–urban edges; and adopt acoustic/visual deterrents and dynamic management within core airport areas to reduce strike risk. Our findings provide a comprehensive baseline of airport bird diversity and bird-strike risk in southwestern China, offering evidence-based guidance for airport ecological safety management and regional biodiversity conservation. Full article

In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption. Full article

This paper presents an adapted methodology for the prediction of fracture loads in additively manufactured (fused filament fabrication) polymers that exhibit non-linear behavior. The approach is based on the Average Strain Energy Density (ASED) criterion, which is typically limited to materials which develop fully linear-elastic behavior. Thus, in those cases where the material has a certain (non-negligible) amount of non-linear behavior, the ASED criterion needs to be corrected. To extend its applicability, this work proposes a thorough calibration of the ASED characteristic parameters: the critical value of the strain energy and the volume of the corresponding control volume. This enables the extrapolation of the linear-elastic formulation to non-linear situations. The approach is validated using acrylonitrile-styrene-acrylate (ASA) and 10 wt.% carbon-fiber reinforced ASA specimens. Single-edge-notched bending (SENB) specimens with three different raster orientations (0/90, 45/−45, and 30/−60) and four U-notch radii (0.0 mm—crack-like, 0.50 mm, 1.0 mm, and 2.0 mm) were printed and tested. The results demonstrate that the proposed calibration of the ASED criterion allows for accurate predictions of failure loads, providing a reliable tool for the structural integrity assessment of 3D-printed components. Full article

The optimization of the microstructural homogeneity and residual stress distribution in Ti sputtering targets for OLED 6G applications is essential for improving dimensional stability, durability, and deposition performance. Herein, 3N Ti plates were hot-rolled at 730 °C and then annealed at 600 °C and 700 °C for different durations to investigate the effects of annealing parameters on microstructural evolution and stress relaxation. X-ray diffraction analysis revealed that hexagonal α-Ti with progressive development of the (002) orientation was produced during annealing under all the conditions. Electron backscatter diffraction analyses showed that short-time annealing at 600 °C (≤30 min) generated heterogeneous grains, high dislocation density, and mixed grain boundary character, whereas extended annealing (≥60 min) produced a more uniform microstructure. However, residual stress differences between the plate center and edge remained significant under this condition. Conversely, annealing at 700 °C promoted progressive recrystallization, as indicated by increased high-angle grain boundary fractions and decreased kernel average misorientation values, and facilitated grain growth stabilization across the plate. Prolonged annealing improved microstructural and residual stress uniformity significantly, and near-complete homogenization was achieved after 5 h. These findings demonstrate that annealing at 700 °C for sufficient time is optimal for producing homogeneous microstructures and uniform residual stress distributions, providing valuable guidelines for Ti sputtering target processing. Full article

The importance of emotional intelligence cannot be neglected when it comes to employees’ behavioral outcomes in the workplace. When organizations deploy Corporate Social Responsibility initiatives that improve their image, they can foster an atmosphere where employees develop loyalty and commitment towards their roles and organizations. This can be more vivid for the case of Small and Medium Enterprises (SMEs) where modern and sustainable strategies are embedded in the strategy to improve the social, economic, and environmental domains of the society they serve to. The current research examines how emotionally intelligent employees can leverage brand image and Corporate Social Responsibility as dynamics that improve their commitment to their roles and their organizations. This leads to better service provision, ultimately improving the company’s reputation, performance, and competitive longevity. Using Smart PLS software and Partial Least Squares–Structural Equation Modeling (PLS-SEM), a total of 184 SME employees were surveyed across forty-two firms operating in Lebanon. The findings suggest that SMEs can establish mechanisms which, under the lens of emotional intelligence, attachment, and sustainability-oriented theories, yield positive work outcomes among employees while facilitating a sustainable competitive edge and improved image both internally and externally. Full article

Early pest detection in edible crops demands sensing solutions that can run at the edge under tight power, budget, and maintenance constraints. This review synthesizes peer-reviewed work (2015–2025) on three modality families—vision/AI, spectroscopy/imaging spectroscopy, and indirect sensors—restricted to edible crops and studies reporting some implementation or testing (n = 178; IEEE Xplore and Scopus). Each article was scored with a modality-aware performance–cost–implementability (PCI) rubric using category-specific weights, and the inter-reviewer reliability was quantified with weighted Cohen’s $\kappa$. We translated the evidence into compact decision maps for common deployment profiles (low-power rapid rollout; high-accuracy cost-flexible; and block-scale scouting). Across the corpus, vision/AI and well-engineered sensor systems more often reached deployment-leaning PCI (≥3.5: 32.0% and 33.3%, respectively) than spectroscopy (18.2%); the median PCI was 3.20 (AI), 3.17 (sensors), and 2.60 (spectroscopy). A Pareto analysis highlighted detector/attention models near $(P,C,I)\approx (4,5,4)$; sensor nodes spanning balanced $(4,4,4)$ and ultra-lean $(2,5,4)$ trade-offs; and the spectroscopy split between the early-warning strength $(5,4,3)$ and portability $(4,3,4)$. The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic $\kappa$ = 0.73–0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: ${\kappa }_{\mathrm{quadratic}}=0.30$–$0.44$), supporting rubric stability with adjacency-dominated disagreements. The decision maps operationalize these findings, helping practitioners select a fit-for-purpose modality and encouraging a minimum PCI metadata set to enable reproducible, deployment-oriented comparisons. Full article

The accurate registration of image pairs is an indispensable key step in the process of disaster assessment, environmental monitoring, and change detection. However, obtaining correct matches from input images is difficult, especially from images with significant resolution and regional variations. The current image-registration algorithms perform poorly in this application scenario. In this article, a spatial point feature-based registration method is proposed for remote sensing images with large regional variations. First, a new edge keypoint extraction method is designed that selects points with gradient magnitude maxima around the neighborhood of the edge line segments as keypoint features. Then, the feature descriptors for each keypoint are constructed based on the geometrical distribution (distance and orientation) of each keypoint. Considering the stability of the distribution of the edge contours, our constructed descriptor vectors can be well used for image pairs with large resolution and regional variations. In addition, all feature descriptors in this method are constructed and matched in the rotated image pyramid. Finally, the fast sampling consensus algorithm is applied to eliminate mismatches. In test images with various scales, rotation angles, and regional variations, the proposed method achieved pixel-level root mean square error, and the average registration precision is nearly 100%. Meanwhile, our proposed method’s rotation and scale invariance are verified by rotating and downsampling the image pairs extensively. In addition, compared with the comparison algorithms, the method proposed in this paper has better registration performance for images with resolution and regional variations. Full article

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence. Full article

This study examines how screw diameter, penetration length, and aperture ratio affect self-tapping screw (STS) withdrawal resistance in bamboo/wood-oriented strand board (WOSB/BOSB) to increase bamboo use in construction and furniture. It proposes a widely applicable empirical formula for calculating withdrawal resistance. With its high specific strength, uniformity, and STS withdrawal resistance, BOSB is a promising material for engineering and furniture applications, according to experiments. Screw diameter, penetration length, and aperture ratio significantly influence the STS’s withdrawal behavior. Among these, screw diameter and penetration length are the primary factors affecting screw withdrawal behavior. As the two factors increase, withdrawal resistance increases linearly. However, the relationship between withdrawal resistance and aperture ratio is non-linear, initially increasing and then decreasing as the aperture ratio increases. With an optimal mounting aperture ratio, the STS withdrawal forces in the BOSB face and edge are approximately 3 and 3.5 times greater than in WOSB, respectively. Traditional formulas for withdrawal resistance were refined based on the fitting equation of aperture ratio and withdrawal force, significantly reducing the relative errors of the modified formulas. Notably, the withdrawal resistance results for STSs calculated using the refined equation based on the CCMC 13677-R standard achieve an accuracy of up to 93%. Full article

The contribution of Higher Education Institutions (HEIs) in achieving UN-Sustainable Development Goals (SDGs) is critical. HEIs’ contribution to the SDGs is not only confined to education and research but also to their engagement with society, policy-ma-king, infrastructure establishment, and sustainability practice in the institution. By aligning their programs and projects to the SDGs, HEIs can share significantly, producing a more sustainable development for all. One of the notable investments of Davao Oriental State University (DOrSU) is the construction of the University Research Complex (UResCom). The structure contains the university’s research laboratories and working spaces for academic scholars to conduct cutting-edge research and scientific breakthroughs. The seed fund for the laboratory equipment and initial research participation was sponsored by project grants from the Department of Science and Technology—Philippine Council for Industry, Energy, and Emerging Technology Research and Development (DOST-PCIEERD). Partnering with established laboratories and institutions is also an effective tool to guide developing universities. This approach will strengthen more productive research engagements as one of the prime movers of the university’s development. It will enhance scientific research outputs and coverage, improve the faculty’s technological competence, enable partnerships with the industrial sector, and is a leverage for the submission of project proposals. This approach will significantly contribute to improving the country’s Global Innovation Index (GII) ranking and to the achievement of these goals. Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article

The crystal structure of kuliokite-(Y), Y₄Al(SiO₄)₂(OH)₂F₅, has been re-investigated using the material from the type locality the Ploskaya Mt, Kola peninsula, Russian Arctic. It has been shown that in contrast to previous studies, the mineral is monoclinic, Im, with a = 4.3213(1), b = 14.8123(6), c = 8.6857(3) Å, β = 102.872(4)°, and V = 541.99(3) ų. The crystal structure was solved and refined to R₁ = 0.030 on the basis of 3202 unique observed reflections. The average chemical composition determined by electron microprobe analysis is (Y_2.96Yb_0.49Er_0.27Dy_0.13Tm_0.07Lu_0.05Ho_0.05Gd_0.01Ca_0.01)_Σ4.04Al_0.92Si_2.04O₈-[(OH)_2.61F_4.42]_Σ7.03; the idealized formula is (Y,Yb,Er)₄Al[SiO₄]₂(OH)_2.5F_4.5. The crystal structure of kuliokite-(Y) contains two symmetrically independent Y sites, Y1 and Y2, coordinated by eight and seven X anions, respectively (X = O, F). The coordination polyhedra can be described as a distorted square antiprism and a distorted pentagonal bipyramid, respectively. The refinement of site occupancies indicated that the mineral represents a rare case of HREE fractionation among two cation sites driven by their coordination numbers and geometry. In agreement with the lanthanide contraction, HREEs are selectively incorporated into the Y2 site with a smaller coordination number and tighter coordination environment. The strongest building unit of the structure is the [AlX₂(SiO₄)₂] chain of corner-sharing AlX₆ octahedra and SiO₄ tetrahedra running along the a axis. The chains have their planes oriented parallel to (001). The Y atoms are located in between the chains, along with the F⁻ and (OH)⁻ anions, providing the three-dimensional integrity of the crystal structure. Each F⁻ anion is coordinated by three Y³⁺ cations to form planar (FY₃)⁸⁺ triangles parallel to the (010) plane. The triangles share common edges to form [F₂Y₂]⁴⁺ chains parallel to the a axis. The analysis of second-neighbor coordination of Y sites allowed us to identify the structural topology of kuliokite-(Y) as the only case of the skd network in inorganic compounds, previously known in molecular structures only. The variety of anionic content in the mineral allows us to identify the potential existence of two other mineral species that can tentatively be named ‘fluorokuliokite-(Y)’ and ‘hydroxykuliokite-(Y)’. Full article

CFRP composite laminates have been widely used in shipbuilding and marine engineering fields, but there is currently a lack of comparative analysis of their blast resistance and dynamic performance under different anisotropic and load conditions. This study aims to characterize the damage response of thick composite laminates with different impact strengths, layer orientations, and laminate thicknesses under water-based explosive loads. By conducting underwater impact tests on laminated panels and combining fluid structure coupling simulations, the study focuses on understanding the deformation and failure mechanisms and quantifying the damage caused by structural properties and loading rates. The results show that while composite laminates show elastic deformation and high recoverability, they are susceptible to matrix tensile damage, particularly at edges and centers. This study reveals that maximum out-of-plane displacement is proportional to impact intensity, while damage dissipation energy is quadratically related. Optimal ply orientations can reduce anisotropy and mitigate damage. Increasing laminate thickness from 3 mm to 8 mm reduces the maximum out-of-plane displacement by 32%, with diminishing returns observed beyond 6 mm thickness. This research offers valuable insights for optimizing composite laminate design to enhance impact resistance and efficiency. Full article

Fibre-reinforced composites are facing new challenges in the context particular in sustainability and recyclability. Vitrimers could be useful as new matrices to support the increase in sustainability. Due to their high strength, which is comparable to that of thermosets often used in composites, and their covalent adaptive networks, which make them reshapeable for scaled-up manufacturing and recycling purposes, they are very useful. Orthogonal cutting is used for precise reshaping and functional integration into carbon fibre reinforced plastics. Vitrimers could improve processing results at the cutting edge as well as surface quality thanks to their self-healing properties compared to brittle matrices, as well as enabling the recycling of formed chips and scrap. This study showcases the manufacturing of a carbon fibre-reinforced vitrimer using 4-aminophenyl disulfide as a hardener, with vacuum-assisted resin infusion. The temperature of chip formation and the cutting parameters are then shown for different fibre orientations, cutting widths and speeds. The observed cutting forces are lower (less than 140 N) and more irregular for fibre orientations 45°/135°, increasing with cutting depth, and fluctuating periodically during machining. Despite varying cutting speeds, the forces remain relatively constant in range between 85 N and 175 N for 0°/90° fibre orientation and 50 N and 120 N for 45°/135° fibre orientation, with no significant tool wear observed and lower-damage depth and overhanging fibres observed for 0°/90° fibre orientation. Damage observation of the cutting tool shows promising results, with lower abrasion observed compared to thermoset matrices. Microscopic images of the broached surface also show good quality, which could be improved by self-healing of the matrix at higher temperatures. Temperature measurements of chip formation using a high-speed camera show a high temperature gradient as cutting speeds increase, but the temperature only ever exceeds 180 °C at cutting speeds of 150 m/min, ensuring reprocessability since this is below the degradation temperature. Therefore, orthogonal cutting of vitrimers can impact sustainable composite processing. Full article

The increasing sophistication of cyberattacks makes Intrusion Detection Systems (IDSs) essential, yet the high dimensionality of modern network traffic hinders accuracy and efficiency. We conduct a comparative study of multi-objective feature selection for IDS using four bio-inspired metaheuristics—Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—on the X-IIoTID dataset. GA achieved the highest accuracy (99.60%) with the lowest FPR (0.39%) using 34 features. GWO offered the best accuracy–subset balance, reaching 99.50% accuracy with 22 features (65.08% reduction) within 0.10 percentage points of GA while using ~35% fewer features. PSO delivered competitive performance with 99.58% accuracy, 32 features (49.21% reduction), FPR 0.40%, and FNR 0.44%. ACO was the fastest (total training time 3001 s) and produced the smallest subset (7 features; 88.89% reduction), at an accuracy of 97.65% (FPR 2.30%, FNR 2.40%). These results delineate clear trade-off regions of high accuracy (GA/PSO/GWO), balanced (GWO), and efficiency-oriented (ACO) and underscore that algorithm choice should align with deployment constraints (e.g., edge vs. enterprise vs. cloud). We selected this quartet because it spans distinct search paradigms (hierarchical hunting, evolutionary recombination, social swarming, pheromone-guided foraging) commonly used in IDS feature selection, aiming for a representative, reproducible comparison rather than exhaustiveness; extending to additional bio-inspired and hybrid methods is left for future work. Full article