Search Results (1,137)

The agricultural industry largely relies on conventional pesticides to maintain healthy, pest-free crops. Application of conventional insecticides is the go-to method for cultivating important food crops, such as corn and sorghum, free of Spodoptera frugiperda (fall armyworm) infestations. However, conventional insecticides have purported negative environmental and health impacts. Natural plant extracts, such as essential oils, are viewed as a promising alternative to conventional insecticides. In the current study, Dysphania ambrosioides (epazote) essential oil was embedded into an artificial diet and fed at two different concentrations to fall armyworms during a 10-day period. Final weights of the 5% epazote treatment group were statistically less (F₆₃₄₃ = 136.2 p < 0.001) than control groups. The 5% epazote treatment group also experienced the highest mortality rate (62%) of any treatment group (X² = 831.4, DF = 6, p < 0.001). Findings suggest that epazote essential oil has potential as an effective, natural insecticidal ingredient. This research is of importance to the fields of agronomy and health sciences. Full article

Automated vision-based sensing for personal protective equipment (PPE) compliance in high-formwork support system (HFSS) construction environments faces three compounding challenges: extreme within-image scale variation, dense scaffold occlusions, and small safety hook targets prone to missed detection. Existing sensing systems address only binary presence detection and cannot assess whether safety harnesses are anchored in compliance with regulatory requirements. This paper proposes YOLO-ILB, a lightweight task-specific object detector built on YOLO11n with three targeted improvements. The C3k2_IDWC module replaces standard convolutions with multi-branch Inception depthwise convolutions, improving multi-scale feature discrimination at reduced computational cost. The SPPF_LSKA module embeds large separable kernel attention into the SPPF aggregation path, strengthening global context awareness to suppress scaffold background interference. A BiFPN neck replaces the original PAN structure, enabling bidirectional cross-scale weighted feature fusion to balance detection of small hooks and large harnesses within a sin gle image. A UAV-based sensing dataset was constructed using a DJI Mini 3 Pro (4032 × 3024 px) across 17 real construction sites under varied altitudes, viewing angles, and illumination conditions, yielding 2700 annotated images across five object categories. YOLO-ILB achieves mAP50 = 0.939 with only 1.923 M parameters and 5.7 G FLOPs at 262.3 FPS, outperforming eight mainstream YOLO baselines while remaining deployable on resource-constrained edge computing nodes. A geometry-based compliance algorithm further classifies three harness anchoring states—correct high anchoring, incorrect low anchoring, and unclipped or excessively distant hook—without additional sensors or annotations, achieving 90.82% overall accuracy on 305 field instances and extending the sensing system from presence detection to regulatory compliance assessment. Full article

LiDAR-based 3D object detection remains challenging due to sparse and irregular point cloud distributions, which degrade detection accuracy for small and occluded objects. In view of this, this paper proposes a novel two-stage voxel-based 3D detector, namely LSCA-RCNN, to address these issues. First, spatial residual blocks (SRBs) and large-kernel spatial-wise convolutions are integrated into the 3D backbone to suppress feature degradation and to expand the receptive fields for stable multi-scale feature learning. Second, a ConvNeXt-based 2D backbone with spatial attention is constructed to enhance discriminative feature representation of small objects. Third, a cascaded detection head embedded with fine-grained grouped convolutions and cross-stage cross-attention is designed to achieve progressive bounding box refinement and to improve localization precision. Extensive evaluations on the KITTI dataset with the R40 metric show that the proposed method achieves consistent performance improvements over the baseline. In the moderate setting, LSCA-RCNN increases the 3D AP by 2.12%, 7.66%, and 5.43% for cars, pedestrians, and cyclists, respectively, while achieving gains of 1.62%, 5.05%, and 7.05% under the hard setting. These results validate the effectiveness and robustness of the proposed LSCA-RCNN for complex and challenging autonomous driving detection tasks. Full article

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Robot control based on visual information perception has been a hot topic in the field of industrial robots, and the use of visual servoing technology to guide robots for high-precision spatial localization of machined workpieces has a wide range of application value. Aiming at the camera hand–eye calibration error and robot repositioning error, which have a large impact on the spatial localization and navigation accuracy, and when the binocular camera Z-direction accuracy is not high enough and the viewing angle is limited, etc., we propose a spatial visual servoing algorithm based on an orthogonal vision system that combines an eye-in-hand camera and an eye-to-hand camera in a hybrid configuration. By extracting sub-pixel image features in real time and deriving directionally decoupled interaction matrices, a linear controller is designed to guide the robot in the XY-plane and Z-direction separately. This decoupling strategy enlarges the convergence domain, avoids local minima caused by coupled degrees of freedom, and enhances system stability. To this end, the intrinsic calibration and hand–eye calibration of two cameras placed orthogonally are carried out firstly, and the accuracy of hand–eye calibration is not too demanding; then the sub-pixel level image position of the target is extracted in real time and the interaction matrix is derived and a linear controller is designed to control the robot’s motion; finally, the experiments of spatial localization accuracy are completed on the KUKA iiwa to validate the effectiveness of the method. Full article

Objectives: To assess the prevalence of maxillary sinus pathology in patients requiring sinus floor elevation to aid dental implant treatment. The study aimed to identify the degree, morphology, and location of mucosal thickening and its relation to ostium patency, as well as any odontogenic pathology contributing to mucosal thickening. Methods: This study was conducted at the ICE Postgraduate Dental Institute and Hospital, England, UK, between February and April 2025. cone-beam computed tomography (CBCT) scans of 20 patients who were partially dentate and edentulous who attended ICE for sinus floor elevation (SFE) between August 2023 and March 2025 were retrospectively examined. Mucosal thickening >2 mm was considered pathological. The CBCT scans of 26 maxillary sinuses were analysed for the following mucosal thickening characteristics: degree, morphology, and location of mucosal thickening, ostium patency, presence of odontogenic pathology, and need for onward referral to otorhinolaryngology. Data was also collected in relation to patient demographics and CBCT parameters. Descriptive statistics were used to summarise the data. Results: Of the 26 sinuses examined in the study, 69% presented with mucosal thickening greater than 2 mm. The incidence of mucosal thickening was higher in male patients. Polypoid mucosal thickening was the most common morphology observed in 46%, and circumferential thickening was the most common location observed in 50%. Ostium obstruction was seen in 12.5% of sinuses that had polypoid, circumferential thickening >10 mm. Other incidental findings included apical pathology, periodontal disease, dental implants breaching the sinus floor, and an antrolith. Conclusions: The study found a high incidence of mucosal thickening in patients undergoing sinus floor elevation. The degree and morphology of mucosal thickening, along with ostium patency and the presence of odontogenic pathology, are important factors to consider in preoperative assessment. Large field of view CBCT scans are required to allow visualisation of the osteomeatal complex. Collaboration between dental implantologists and otorhinolaryngologists is crucial for managing patients with sinus pathology and ensuring successful outcomes in SFE. Further larger prospective studies with clinical correlation are needed to better evaluate the association between sinus pathology, ostium patency, and sinus floor elevation outcomes. Full article

Small surface defects on large structures such as wind turbine blades, bridges, and pipelines pose significant safety risks if left undetected. Therefore, a laser triangulation system is designed for contactless surface geometry inspection from a flying drone at a working distance of 2 m. To enable single-shot triangulation measurements in dynamic aerial environments, a single-shot-capable approach is realized by means of a laser and a diffractive optical element for creating a dot-matrix illumination pattern and a camera for image recording. The setup, with 101 × 101 measurement points, is calibrated by using an interferometer as a reference, which shows a sub-pixel resolution capability. As a result, the depth resolution capability for each point amounts to 126 µm, while the lateral resolution capability is determined by the laser spots’ size of 0.6 mm and the spots’ interspacing of 1.75 mm. With the present configuration, unambiguous depth detection is possible for local surface gradients of up to 2.3 times the interspot distance between adjacent measurement points, and the field of view is 17.56 cm × 17.56 cm. Finally, surface defects with lateral sizes on the order of 1 cm and 0.5 cm are currently detectable, as is demonstrated by experimental results from in-flight measurements. Thus, the potential and challenges of single-shot laser triangulation for drone-based inspection in real-world scenarios are presented. Full article

Continuous ground-based microwave (MW) measurements with the RPG-HATPRO radiometer at the observational site of St. Petersburg State University located near the coastline of the Gulf of Finland have provided a large amount of data on the cloud liquid water path (LWP) of non-raining clouds. The 12-year (2013–2024) time series of the LWP values has been analysed and the diurnal evolution of the LWP has been assessed for each month of the year. The calculations have been made for the LWP in the range 0–0.4 kg m⁻² using different sampling subsets that include the so-called true and virtual LWP values. True LWP values correspond to measurements with clouds in the field of view of the radiometer, whereas virtual LWP values correspond to measurements with clouds or with clear sky in the field of view of the instrument and, therefore, virtual values can be zero (in clear sky cases). Based on the correlation analysis, time periods characterised by similar meteorological conditions and suitable for assessing the daily dynamics of LWP were identified. The LWP diurnal cycles in December, January, and February demonstrated a similar pattern with a maximum around local astronomical noon and with a minimum around midnight. For the remaining months except March and June, the maximum LWP is observed in the early morning and the minimum is observed in the afternoon. This cycle is characteristic of marine stratocumulus clouds. The diurnal cycles of the LWP in March and June, peaking in the afternoon and morning, respectively, are typical of convective continental clouds. Thus, the LWP diurnal cycle in the coastal zone of the Gulf of Finland may have characteristics of both marine and continental clouds. Parameters of the two-mode sinusoidal approximation of the diurnal cycle of the LWP in different seasons are presented. Full article

In agricultural machinery visual navigation, accurately identifying the navigation line extraction region (NLER) at the center of the field of view is crucial for obtaining a precise navigation centerline. Although deep learning is the predominant method for NLER extraction, existing approaches face challenges in farmland environments characterized by densely distributed and irregularly extended leaves. These challenges result in unstable predictions, slow inference, and large model sizes that impede real-time applications. To address these issues, we propose a lightweight navigation segmentation residual network (LNS-ResNet), which integrates an inhibition–enhancement module (IEM) and a global convolutional residual block (GCRB). The IEM uses row–column one-dimensional convolutions to enhance vertical features between crop rows and suppress leaf-edge interference, producing more robust input features. The GCRB incorporates a full convolutional global attention (FCGA) mechanism to capture global context while preserving local spatial information. LNS-ResNet effectively reduces foliage interference and achieves accurate segmentation, with intersection over union (IoU) scores of 84.71% for crop row and 93.77% for path regions. Based on the segmentation output, we further propose a mask region determination-based navigation line extraction algorithm (MRD-Line), which directly identifies the NLER and connects the centerline within the mask without relying on line fitting. Deployed experiments on the Jetson TX2 demonstrate that the proposed method achieves both accuracy and efficiency, with mean angular deviations of 0.138° (path) and 0.425° (crop row), with average processing times of 64.1 ms (path) and 62.6 ms (crop row). Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with growing applications in pediatric cardiology. AI techniques, particularly machine learning and deep learning, enable the analysis of complex and heterogeneous data, supporting diagnosis, risk stratification, and clinical decision-making. This paper provides an overview of current AI applications in this field, discusses existing challenges, and explores future perspectives. In pediatric cardiology, AI has shown promising results across multiple domains. In electrocardiography, AI algorithms improve diagnostic accuracy and enable early detection of cardiac conditions, even in asymptomatic patients, while facilitating telecardiology-based care pathways. In cardiac auscultation, AI-assisted digital stethoscopes enhance the distinction between innocent and pathological murmurs, supporting primary care physicians and optimizing referral to pediatric cardiologic centers. Multimodality imaging represents one of the most advanced areas of AI applications. In echocardiography, magnetic resonance and computed tomography, AI improves image acquisition, view classification, and automated quantification, contributing to more standardized and reproducible assessments. Additionally, emerging technologies such as virtual reality, integrated with AI, offer innovative tools for education, surgical planning, and patient-specific modelling. Despite these advances, several limitations remain, including limited availability of large pediatric datasets, challenges in model generalizability and issues related to interpretability and integration into clinical workflows. In conclusion, AI represents a powerful complementary tool in pediatric cardiology, with the potential to improve diagnostic accuracy, optimize healthcare resources and support the transition toward precision medicine. Full article

In the field of traditional aging state evaluation of paper materials, traditional detection technologies such as ink drop method and chemical analysis have inherent limitations including sample damage, strong subjectivity, and inability to realize large-area detection. To address these problems, a non-contact and non-destructive testing method based on air-coupled ultrasonic technology was developed in this study, to achieve objective and quantitative characterization of paper roughening degree and ink wettability. The system adopted a LabVIEW-based host computer to control scanning and signal acquisition. Based on the propagation and scattering mechanism of ultrasound in the porous fiber structure of paper, the amplitude difference and pixel distribution of C-scan images were extracted as core characteristic parameters. The experimental results show that, with a 400 kHz air-coupled probe and 200 mm/s scanning speed, the roughening degree of paper can be quantitatively characterized by the amplitude difference of ultrasonic transmission signals. The amplitude difference increases significantly with the rise of water content, and the difference in roughening characteristics between newsprint and Xuan paper can be clearly distinguished. The ink wettability can be judged by the pixel distribution of the C-scan image: the higher the proportion of intermediate color pixels, the closer the ink circularity is to 1, and the better the ink wettability. All test results are highly consistent with the national standard GB/T 18739-2008. The constructed air-coupled ultrasonic testing system can provide reliable technical support for quality control and aging evaluation of paper cultural relics and high-grade paper by characterizing both surface roughening and internal porous structure (which are coupled during paper aging), without any contact or damage to the samples. Full article

This paper presents a dual-axis sun sensor employing a cross-slit aperture in conjunction with a four-quadrant trapezoidal photodiode layout. The cross-slit configuration enhances angular sensitivity and resolution, while the trapezoidal photodiode geometry preserves a high signal-to-noise ratio at both near-normal incidence and large Sun angles, maintaining reliable directional discriminability around normal incidence. Compared with conventional quad-triangle photodiode layouts, the proposed trapezoidal geometry avoids the rapid collapse of the illuminated area near the triangular apex at large incidence angles, thereby preserving signal margin near the field-of-view boundary. System-level optical verification demonstrates that, after calibration, the proposed sensor achieves an angular accuracy of ±0.3° (3$\sigma$). To mitigate performance variations induced by temperature drift, an embedded shielded dummy photodiode is incorporated to provide a dark-current reference for compensation. Unlike compensation approaches that mainly rely on pre-characterization or offline calibration, the embedded shielded dummy photodiode provides an in situ, real-time dark-current reference for compensating for temperature-induced signal drift in the actual operating environment. Experimental results under dark conditions indicate that the embedded dummy photodiode served as a dark-current reference for compensating the temperature-dependent dark-current variation in the active photodiodes, reducing the peak-to-peak dark-signal variation by 96% over a temperature range from 20 °C to 120 °C. Furthermore, a pyramid-type sun-sensor architecture is proposed by integrating the dual-axis fine sun sensor with four wide-field coarse sun sensors. This system-level configuration extends the effective Sun field of view from the conventional 120°–180° range to approximately 280°, enabling near-hemispherical Sun-angle observability for enhanced attitude determination robustness. Full article

This study examines recent research trends in the use of serious games for science education through a bibliometric analysis of 340 articles and a qualitative content analysis of 56 studies published between 2020 and 2025 in the Web of Science Core Collection. By combining these approaches, the study provides a comprehensive view of both research patterns and how serious games are designed and used in science education. The findings indicate that the field is maturing, with research moving beyond general effectiveness toward understanding how serious games support learning in different contexts. Most studies report positive effects compared to traditional instructional methods. However, results vary across contexts and depend on factors such as design, implementation, and learner characteristics. Research is mainly focused on higher education and is largely driven by leading countries such as the USA and China, although participation from developing countries is increasing. The growing use of immersive technologies, such as augmented and virtual reality, offers new opportunities for interactive and multimodal learning but may also increase cognitive load in certain contexts. There is also growing interest in non-digital games, which have received limited attention despite their effectiveness. Overall, the findings show that more systematic research and clearer design frameworks are needed to better understand how serious games can be used in science education. Full article

Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene, together with the blurred contours and low contrast of infrared images, severely degrades the performance of gas detection and leakage region segmentation. To address these challenges, this paper proposes a gas leak detection method that integrates gas characteristics with spatiotemporal information. Specifically, the non-solid characteristics of gas are incorporated to constrain the foreground extraction process of the Gaussian Mixture Model (GMM), thereby suppressing interfering moving objects. Furthermore, by exploiting the spatiotemporal information in infrared image sequences, a multi-scale cross-attention fusion model is designed to fuse multi-scale and global feature representations, improving the accuracy of foreground detection. Finally, density-based clustering is employed to achieve complete segmentation of gas regions with irregular shapes. Experimental results demonstrate that the proposed method effectively suppresses interference from solid objects, accurately detects gas leakage, and successfully segments the diffusion regions. Compared with existing approaches, the proposed method shows significant advantages and provides a valuable reference for research on infrared imaging-based gas leak detection. Full article

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric Detail–Component Complexity–Texture Appearance–Semantic Information (B-D-C-T-S) framework organizes model representations into five separately addressable and schedulable dimensions, covering spatial proxies, geometry, components, textures, and semantics. A compact 16-bit structured state word is used to represent runtime states and reduce dependence on repeated text-based state parsing, supporting fixed-offset bitwise decoding, exclusive-OR (XOR)-based differencing, constraint checking, and incremental updating. A centroid-assigned Home Tile strategy is further introduced to reduce redundant semantic payloads for cross-tile objects. The method was evaluated using a single-building BIM model and an urban-scale photogrammetric mesh dataset. Under the tested initial-view setting, staged decoupled loading reduced the first-screen requested payload by 93.1% compared with monolithic loading. State-word-based C-field extraction achieved an approximately 144-fold speedup over JSON deserialization and C-field lookup. The Home Tile strategy reduced the total semantic payload by 44.1% in the semantic-redundancy test. In the 1.12 GB first-screen memory test, state-word-driven D1 tile scheduling loaded only 22.7 MB of physical payload, with stable resident memory of approximately 88.1 MB. These results indicate that the proposed method supports object-level state representation, selective resource activation and scheduling, Home Tile semantic routing, incremental updating, and first-screen memory control within tiled Web3D pipelines. Full article