Search Results (1,562)

Accurate extrinsic calibration between optical sensors, such as camera and LiDAR, is crucial for multimodal perception. Traditional methods based on specific calibration targets exhibit poor robustness in complex optical environments such as glare, reflections, or low light, and they rely on cumbersome manual operations. To address this, we propose a fully unsupervised, end-to-end calibration framework. Our approach adopts a dual-Transformer architecture: a Vision Transformer extracts semantic features from the image stream, while a Point Transformer captures the geometric structure of the 3D LiDAR point cloud. These cross-modal representations are aligned and fused through a neural network, and a regression algorithm is used to obtain the 6-DoF extrinsic transformation matrix. A multi-constraint loss function is designed to enhance structural consistency between modalities, thereby improving calibration stability and accuracy. On the KITTI benchmark, our method achieves a mean rotation error of 0.21° and a translation error of 3.31 cm; on a self-collected dataset, it attains an average reprojection error of 1.52 pixels. These results demonstrate a generalizable and robust solution for optical-sensor extrinsic calibration, enabling precise and self-sufficient perception in real-world applications. Full article

A growing international consensus holds that science education must advance beyond content coverage to cultivate robust understanding of the Nature of Science (NoS)—how scientific knowledge is generated, justified, revised, and socially negotiated. Yet naïve conceptions persist among students and teachers, and effective, scalable classroom strategies remain contested. This narrative review synthesizes research and practice on games and playful activities that make epistemic features of science visible and discussable. We organize the repertoire into six families—(i) observation–inference and discrepant-event tasks; (ii) pattern discovery and rule-finding puzzles; (iii) black-box and model-based inquiry; (iv) activities that dramatize tentativeness and anomaly management; (v) deliberately underdetermined mysteries that cultivate warrant-based explanations; and (vi) moderately contextualized games. Across these designs, we analyze how specific mechanics afford core NoS dimensions (e.g., observation vs. inference, creativity, plurality of methods, theory-ladenness and subjectivity, tentativeness) and what scaffolds transform playful engagement into explicit, reflective learning. We conclude with pragmatic guidance for teacher education and curriculum design, highlighting the importance of language supports, structured debriefs, and calibrated contextualization, and outline priorities for future research on equity, assessment, and digital extensions. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

Above-ground biomass in agroforestry refers to the total mass of living vegetation, primarily trees and shrubs, integrated into agricultural landscapes. It plays a key role in climate change mitigation by capturing and storing carbon. Accurate estimation of above-ground biomass in agroforestry systems requires effective drone deployment and sensor management. This study presents a detailed methodology for biomass estimation using Unmanned Aircraft Systems, based on an experimental campaign conducted in the Campania region of Italy. Multispectral drone platforms were used to generate calibrated reflectance maps and derive vegetation indices for biomass estimation in agroforestry landscapes. Integrating field-measured tree attributes with remote sensing indices improved the accuracy and efficiency of biomass prediction. Following the assessment of mission parameters, flights were conducted using a commercial drone to demonstrate consistency of results across multiple altitudes. Terrain-follow mode and high image overlap were employed to evaluate ground sampling distance sensitivity, radiometric performance, and overall data quality. The outcome is a defined process that enables agronomists to effectively estimate above-ground biomass in agroforestry landscapes using drone platforms, following the procedure outlined in this paper. Predictive performance was evaluated using standard model metrics, including R², RMSE, and MAE, which are essential for replicability and comparison in future studies. Full article

This study presents a comparative performance modeling and optimization framework for a 5 kWp rooftop photovoltaic (PV) system in Qatar, using two widely adopted simulation tools, PVsyst and the System Advisor Model (SAM). The research addresses a key limitation in existing PV modeling practice: the restricted capability of standard software to represent site-specific soiling and dynamic albedo effects under arid climatic conditions. To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. Additionally, the study utilized dynamic albedo inputs to better account for ground-reflectance effects in energy yield simulations. The optimized approach reduced the root mean square error (RMSE) of predicted soiling ratios from 7.30 to 1.93 and improved the agreement between simulated and measured monthly energy yields for 2024, achieving normalized RMSE values of 4.66% in SAM and 4.86% in PVsyst. The findings demonstrate that coupling data-driven soiling optimization with refined albedo representation modernizes the predictive capabilities of PVsyst and SAM, yielding more reliable performance forecasts. This methodological advancement supports better-informed design and operation of rooftop PV systems in desert environments where soiling and reflectivity effects are pronounced. Full article

Objectives: Dental eruption is a complex process influenced by various factors, including endocrine factors as growth hormone (GH). The aim of this study was to assess differences in the advancement of tooth eruption between growth hormone-deficient (GHD) and idiopathic short-statured (ISS) children and a control group of children with normal growth patterns. Methods: A total of 156 children participated in this study: 78 patients with short stature (50 boys and 28 girls) and 78 healthy and age- and sex-matched control subjects. Each permanent tooth was classified according to its clinical eruption stage by one trained and calibrated dentist. Results: The mean age was 10.22 ± 2.42 years for the study and 10.15 ± 2.45 for the control group. In our study, we observed eruption delay during the early mixed dentition stage. A significant difference was found in the degree of eruption for all incisors and first permanent molars between the GHD before treatment group and the control group (p = 0.045). The difference was apparent at the initial stage of permanent tooth eruption, in the group of children who had not yet initiated growth hormone treatment. The eruption of remaining tooth groups did not differ significantly between the children with growth failure and the control group (p > 0.05). Conclusions: Our findings indicate that the delay in tooth eruption observed in short-statured children, particularly affecting the first permanent molars and incisors, may reflect the direct influence of growth hormone deficiency on early dental development. The clinical relevance of this finding underlines the importance of individualized dental care and careful timing of orthodontic assessments in short-statured patients, especially prior to the initiation of GH therapy. Full article

Ensuring the quality and safety of agricultural and food products is crucial for protecting consumer health, meeting market expectations, and complying with regulatory requirements. Quality and safety parameters are commonly assessed using chemical and microbiological analyses, which are time-consuming, impractical, and involve the use of toxic solvents, often disrupting the material’s original structure. An alternative technique, infrared spectroscopy, including near-infrared (NIR), mid-infrared (MIR), and short-wave infrared (SWIR), has emerged as a rapid, powerful, and minimally destructive technique for evaluating the quality and safety of food and agricultural products. This review focuses on discussing MIR spectroscopy, particularly Fourier transform infrared (FTIR) techniques, with emphasis on the attenuated total reflectance (ATR) measurement mode (globar infrared light source is commonly used) and on the use of synchrotron radiation (SR) as an alternative high-brightness light source. Both approaches enable the extraction of detailed spectral data related to molecular and functional attributes concerning quality and safety, thereby facilitating the assessment of crop disorders, food chemical composition, microbial contamination (e.g., mycotoxins, bacteria), and the detection of food adulterants, among several other applications. In combination with advanced chemometric techniques, FTIR spectroscopy, whether employing ATR as a measurement mode or SR as a high-brightness light source, is a powerful analytical tool for classification based on attributes, variety, nutritional and geographical origins, with or without minimal sample preparation, no chemical use, and short analysis time. However, limitations exist regarding calibrations, validations, and accessibility. The objective of this review is to address recent technological advancements and existing constraints of FTIR conducted in ATR mode and using SR as a light source (not necessarily in combination). It defines potential pathways for the comprehensive integration of FTIR and chemometrics for real-time quality and safety monitoring systems into the global food supply chain. Full article

Corrosion of rock bolts in engineering exhibits random spatial distribution characteristics. To elucidate the influence mechanism of stochastic corrosion on the surface of rock bolts on the propagation behavior of ultrasonic guided waves, this study establishes a finite element model of rock bolts that incorporates stochastic corrosion characteristics. The coupled effects of corrosion depth and area ratio on guided wave propagation characteristics, time-domain response, energy distribution, and wave velocity variation are systematically investigated. Results indicate that corrosion depth and area ratio synergistically deteriorate guided wave morphology, transforming the stress field from symmetric and uniform to asymmetric and spiral. Reflections, scattering, and mode conversion induced by defects lead to a significant increase in the attenuation rate of pulse amplitude, with the two parameters governing the vertical interaction intensity and horizontal interference scope, respectively. Analysis of the Hilbert curve reveals that corrosion characteristics disrupt energy concentration. Under constant corrosion depth, an increase in area ratio disperses energy toward delayed scattered waves, while under constant area ratio, greater corrosion depth reduces the peak amplitude of the envelope curve. Overall, the energy integral exhibits an increasing trend with the degree of corrosion, whereas the peak-to-peak wave velocity shows a declining trend. The established multivariate nonlinear model accurately describes the coupled influence of corrosion parameters on wave velocity. This stochastic corrosion model overcomes the limitations of traditional simplified models and provides critical theoretical support for parameter calibration and engineering application of ultrasonic guided wave technology in the quantitative assessment of rock bolt corrosion. Full article

Background and Objectives: Post-surgical central nervous system (CNS) infections are severe complications associated with high morbidity and mortality. Vancomycin is a key antibiotic used in their management. However, because of the restrictive properties of the blood–brain barrier (BBB), plasma concentrations may not accurately reflect drug exposure in the brain extracellular fluid (ECF), the presumed site of infection. Cerebral microdialysis enables direct measurement of unbound drug levels in brain ECF. This study aimed to assess vancomycin penetration into brain ECF in patients with suspected or confirmed post-surgical CNS infection. Materials and Methods: Five patients with suspected or confirmed post-surgical CNS infections were enrolled. Paired brain ECF microdialysate and plasma samples (and cerebrospinal fluid (CSF) samples, when available) were collected over two consecutive days at vancomycin steady state. Vancomycin concentrations were determined using a homogeneous enzyme immunoassay and corrected for probe recovery based on in vitro calibration. Pharmacokinetic parameters, including mean concentrations and 24-h area under the concentration–time curve (AUC24), were calculated for plasma and ECF, and ECF-to-plasma ratios were derived. Results: Two subgroups could be identified: patients with negligible ECF concentrations (“low penetrators”), and those with higher ECF levels (“high penetrators”). Mean (SD) ECF-to-plasma concentration ratios were 0.07 (0.04) in “low penetrators” and 0.44 (0.10) in “high penetrators”. The corresponding AUC24 ratios were 0.06 (0.03) and 0.40 (0.03), respectively. The presence of systemic inflammatory response syndrome (SIRS) was considered the most plausible factor differentiating these two subgroups. Conclusions: Vancomycin exposure in brain ECF demonstrated substantial interpatient variability in post-surgical CNS infections, with some patients showing minimal drug penetration. Full article

The dynamic variation of the incidence angle between the millimeter-wave (MMW) fuzes and non-spherical raindrops significantly affects detection performance. To address this issue, the influence of incidence angle on attenuation coefficient, volume reflectivity, and the signal-to-clutter-plus-noise ratio (SCNR) is systematically analyzed by employing the realistic raindrop morphology described by the Beard and Chuang (BC) model and the invariant imbedding (IIM) T-matrix method. By integrating worst-case analysis, the critical incidence angle corresponding to the most severe performance degradation is identified, and the corresponding attenuation coefficient, volume reflectivity, and SCNR values are reconstructed. Numerical simulations demonstrate that for the BC model, the most severe impact on MMW signal propagation occurs at an incidence angle of 180°. Under this condition, the reconstructed attenuation coefficient and volume reflectivity increase by 45.88% and 28.27%, respectively, while the SCNR decreases by 27.35% at 60 GHz operating frequency and 100 mm/h rainfall rate, compared to the spherical raindrop model. This study provides a theoretical basis for calibrating design margins and optimizing anti-interference strategies for MMW fuzes operating in complex meteorological conditions. Full article

With rapid advancements in large language models for natural language processing, their role in semantic-aware process mining is growing. We study semantics-aware process mining, where decisions must reflect both event logs and textual rules. We propose an online, adaptive multi-agent framework that operates over a single knowledge base shared across three tasks—semantic next-activity prediction (S_NAP), trace-level semantic anomaly detection (T_SAD), and activity-level semantic anomaly detection (A_SAD). The approach has three key elements: (i) cross-task corroboration at retrieval time, formed by pooling in-domain and out-of-domain candidates to strengthen coverage; (ii) feedback-to-index calibration that converts user correctness/usefulness into propensity-debiased, smoothed priors that immediately bias recall and first-stage ordering for the next query; and (iii) stability controls—consistency-aware scoring, confidence gating with failure-driven query rewriting, task-level trust regions, and a sequential rule to select the relevance–quality interpolation. We instantiate the framework with Mistral-7B-Instruct, Llama-3-8B, GPT-3.5, and GPT-4o and evaluate it using macro-F1. Compared to in-context learning, our framework improves S_NAP, T_SAD, and A_SAD by 44.0%, 15.6%, and 7.1%, respectively, and attains the best overall profile against retrieval-only and correction-centric baselines. Ablations show that removing index priors causes the steepest degradation, cross-task corroboration yields consistent gains—most visibly on S_NAP—and confidence gating preserves robustness to difficult inputs. The result is immediate serve-time adaptivity without heavy fine-tuning, making semantic process analysis practical under drift. Full article

To meet the growing demands for competency-based and personalized instruction in high school English reading, this study investigates a quantitative approach to modeling learning pathways and progressions. Traditional assessments often fail to capture students’ fine-grained cognitive differences and provide limited guidance for individualized teaching. Based on cognitive diagnostic theory, this study analyzes large-scale empirical data to construct a progression framework reflecting both the sequencing of cognitive skill development and the hierarchical structure of reading abilities. A Q-matrix was calibrated through expert consensus. A hybrid cognitive diagnostic model was used to infer students’ knowledge states, followed by cluster analysis and item response theory to define progression levels, which were mapped to national curriculum standards. The findings reveal that students’ mastery of cognitive attributes follows a stepwise developmental pattern, with dominant learning trajectories. The constructed learning progression aligns well with curriculum-based academic quality levels, while uncovering potential misalignments in the positioning of some skill levels. Students with identical scores also showed significant variation in cognitive structures. The proposed model provides a data-informed foundation for adaptive instruction and offers new tools for personalized learning in English reading comprehension. Full article

Accurately evaluating and quantifying microbial spoilage on wine grapes is a major challenge, especially in machine-harvested fruit that is no longer intact as a cluster when it arrives at the winery and cannot be visually inspected. The goal of the study was, using an infrared spectroscopy-based analytical system, to establish a unitless quantitative number that would reflect complex microbial spoilage and could be applied for all types of grapes with only one calibration model. Grapes (cultivars Riesling, Chenin Blanc, Chardonnay, Zinfandel and Petite Sirah) were hand-harvested in two consecutive vintages and separated in the vineyard into visually healthy clusters and infected grapes. Grapes were blended with increasing infection levels between 0 and 20% and models were created using known spoilage indicators. The resulting formula can be used to calculate a weighted index that reflects the microbial infection status of the grape material. Using a common spectroscopy instrument that is already present in larger wineries, the Grape Health Index allows for a quantitative quality assessment within two minutes at the test stand before the grape material is accepted. When visual inspection is not an option, this can help to make data-based quality and blending decisions at a very early stage. Full article

This study introduces and validates a miniaturized testing methodology for the complete orthotropic characterization of structural plywood, including out-of-plane directions that are typically difficult to access. Novel small-scale geometries were developed for tension and shear configurations, with compliance corrections applied to ensure accurate stress–strain responses. The method proved reliable and sensitive to mechanical differences arising from veneer architecture, adhesive type, and interfacial bonding. Two sets of 18 mm structural plywood panels—manufactured with distinct adhesive systems, one bio-based (F1) and one phenol-formaldehyde (F2)—were systematically tested under tensile, compressive, and shear loading in ten orthogonal configurations (T_x, T_y, T_z, C_x, C_y, C_z, τ_xy, τ_yx, τ_xz, τ_yz), following standards NCh 3617, EN 789, and ASTM B831. Tensile moduli were approximately twice the corresponding compressive values, while out-of-plane moduli reached only 6–11% of in-plane values. F1 exhibited higher stiffness in both tension and compression, particularly in transverse directions, due to thicker perpendicular veneers enhancing bending restraint and shear coupling. In contrast, F2 achieved greater peak shear strength owing to its more uniform veneer structure, which improved stress distribution and delayed interlaminar failure. Observed asymmetry between tension and compression reflected microstructural mechanisms such as fiber alignment and cell-wall buckling. The miniature-specimen data provide reliable input for constitutive calibration and finite-element modeling, while revealing clear links between veneer-thickness distribution, shear-transfer efficiency, and macroscopic performance. The proposed framework enables efficient, reproducible orthotropic characterization for optimized, lightweight, and carbon-efficient timber systems. Full article

In Korea, apple (Malus domestica) is one of the major fruit crops. The area occupied by apple orchards has exhibited a consistent upward trend, increasing from 26,398 hectares in 2003 to 33,313 hectares in 2024, and production reached 460,088 tons in 2024. However, stable apple production is currently threatened by global challenges such as climate change and the decline in rural labor, which hinders timely and efficient orchard management. Under these circumstances, developing automated and data-driven technologies capable of rapidly predicting and responding to apple growth conditions is essential to enhancing management efficiency and ensuring consistent fruit quality and yield stability. In this study, unmanned aerial vehicle (UAV)-based multispectral imagery was acquired and used to analyze time series data. Vegetation indices (VIs) derived from this imagery were then applied to build models predicting fruit diameter and length, which reflect apple size. A total of nine VIs were calculated from the acquired data and utilized as input variables for model development. Based on these variables, four machine learning models—Gaussian process regression (GPR), the K-Nearest Neighbors (KNNs), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB)—were developed to predict the fruit diameter and length. Both RFR and XGB showed tendencies of overfitting, and although the KNNs demonstrated relatively stable performance (diameter: R² ≥ 0.82, RMSE ≤ 7.61 mm, RE ≤ 12.53%; length: R² ≥ 0.76, RMSE ≤ 8.85 mm, RE ≤ 15.08%), this model failed to follow the prediction line consistently. In contrast, GPR maintained stable performance in both the validation and calibration stages (diameter: R² ≥ 0.79, RMSE ≤ 8.23 mm, RE ≤ 13.56%; length: R² ≥ 0.72, RMSE ≤ 9.48 mm, RE ≤ 16.16%) and followed the prediction line relatively well, indicating that it is the most suitable model for predicting apple size. These results demonstrate that UAV-based multispectral imagery, combined with machine learning techniques, is an effective tool for predicting the size of apples, and it is expected to contribute to orchard management at different growth stages and improve apple productivity in the future. Full article