Search Results (245)

This practice-led research examines how virtual production represents a circular return to scenographic practice, reactivating integrated modes of spatial authorship that have long underpinned screen storytelling but were obscured by industrial fragmentation. Drawing on a single-day intensive workshop at the Australian Film, Television and Radio School (AFTRS), the study analyses how spatial authorship emerged through embodied, collaborative engagement with an LED volume environment. Grounded in scenographic theory and concepts of distributed cognition and situated authorship, the article reframes virtual production as a condition that renders pre-digital, collaborative modes of making visible within contemporary screen production. The LED volume functions simultaneously as scenic environment, lighting instrument, and compositional partner, requiring participants to negotiate space, light, movement, and camera as a unified spatial event. Analysis identifies how scenographic understanding emerged through virtual scouting, world-responsive storytelling, physical-digital integration, and embodied realisation. The findings extend production design theory by challenging ocular-centric models of mise-en-scène and positioning scenographic integration as screen practice—an epistemic mode of enacting through collective, materially grounded spatial experimentation. While situated within an educational context, the study points to broader implications for how spatial authorship and collective practice are understood in contemporary screen production. Full article

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions. Full article

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate and reduces efficiency due to micro-climatic lighting and field clutter, among other factors. StrawberryDualNet is a framework that supports Integrated Pest Management and automates symptom surveillance. We present dual-path CNN–Transformer fusion design that integrates two branches: a dynamic multi-scale convolution and a lightweight transformer. The former is able to capture fine-grained morphological lesion textures, while the latter captures overall contextual patterns. The two representations are fused through a learnable gating mechanism to decrease visual uncertainty amongst differing symptoms. We used a stratified five-fold cross-validation to evaluate the framework among five economically significant pathogens. Our approach significantly outperformed other automated scouting baselines, achieving 95.1% accuracy and 95.3% precision, respectively, and it is successful for Anthracnose, Gray Mold, Powdery Mildew, Rhizopus Rot, and Black Spot. The model is also scaled down compared to others (0.04 M parameters; 0.72 MB, 13–20× smaller than MobileNetV2/ShuffleNetV2) and is thus able to be deployed on devices that are lacking computational resources. For edge feasibility, we assessed reduced-precision inference; 16-bit floating point quantization preserved baseline performance at 83 FPS, whereas 8-bit integer quantization caused notable accuracy degradation. Overall, the proposed local–global fusion design provides an accurate, interpretable, and scalable tool for real-time disease phenotyping in precision horticulture. Full article

Background: Host cell proteins (HCPs) are process-related impurities that must be monitored in biopharmaceutical products due to their potential impact on product quality and patient safety. Targeted LC–MS/MS approaches such as multiple reaction monitoring (MRM) enable protein-specific HCP quantification but are difficult to apply in highly multiplexed assays because of retention time (RT) variability across complex bioprocess matrices. Methods: Here, we show that conventional RT-scheduled MRM workflows lack transferability when applied to heterogeneous drug substances and process intermediates. Using a targeted assay comprising 240 peptides corresponding to 97 CHO-derived HCPs, RT shifts of several minutes resulted in truncated chromatographic peaks and peptide signal loss, even when wide scheduling windows were used. To overcome this limitation, a scout-triggered MRM (st-MRM) acquisition strategy based on event-driven monitoring was implemented. Results: This approach enabled robust peptide detection across diverse matrices within a single injection, without method re-optimization. Absolute quantification using stable isotope-labeled peptides spanned six orders of magnitude, with HCPs quantified down to 2.9 ppm in purified drug substances. Conclusion: Overall, st-MRM improves the robustness and transferability of highly multiplexed targeted proteomics workflows for HCP analysis. Full article

Female soccer is rapidly becoming a widely practiced sport at different levels: this opens up a new demand for systems meant to protect athletes from head impacts or to monitor their effects. The market is offering some solutions in similar sports, but the specificity and high relevance of soccer encourage the development of a dedicated solution. From market analysis, technology scouting, and ethnographic research a set of functional and technical requirements have been defined and proposed. The designed instrumented head band is equipped with one Inertial Measurement Unit (IMU) in the occipital area and four contact pressure sensors on the sides. The concept design is low-cost and open-architecture, prioritizing accessibility over complexity. The modularity also ensures that each component (sensing, battery, communication) can be replaced or upgraded independently, enabling iterative refinement and integration into future sports safety systems. In addition to safety monitoring for injury prevention or detection of the traumatic impact, the system is relevant for supporting performance monitoring, rehabilitation or post-injury recovery and other important applications. System engineering has started and the next step is building the prototypes for testing and validation. Full article

Soybean, Glycine max (L.) Merrill, is usually grown on a large scale, with pest control based on chemical insecticides. However, the overuse of chemicals has led to several adverse effects requiring more sustainable approaches to pest control. Results from Integrated Pest Management (IPM) employed on Brazilian soybean farms indicate that adopters of the technology have reduced insecticide use by approximately 50% relative to non-adopters, with yields comparable to or slightly higher than those of non-adopters. This reduction can be explained not only by the widespread use of Bt soybean cultivars across the country but also by the adoption of economic thresholds (ETs) in a whole Soybean-IPM package, which has reduced insecticide use. However, low refuge compliance has led to the first cases of pest resistance to Cry1Ac, thereby leading to the return of overreliance on chemical control and posing additional challenges for IPM practitioners. The recent global agenda for decarbonized agriculture might help to support the adoption of IPM since less chemical insecticides sprayed over the crops reduces CO₂-equivalent emissions from its application. In addition, consumers’ demand for less pesticide use in food production has favored the increased use of bio-inputs in agriculture, helping mitigate overdependence of agriculture on chemical inputs to preserve yields. Despite the challenges of adopting IPM discussed in this review, the best way to protect soybean yield and preserve the environment remains as IPM, integrating plant resistance (including Bt cultivars), ETs, scouting procedures, selective insecticides, biological control, and other sustainable tools, which help sustain environmental quality in an ecological and economical manner. Soon, those tools will include RNAi, CRISPR-based control strategies, among other sustainable alternatives intensively researched around the world. Full article

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of early stress symptoms from plant images. However, deep learning models are often opaque, relying on millions of parameters to extract complex nonlinear features that are not interpretable by growers. Recently, eXplainable AI (XAI) techniques have been used to identify key spatial regions that contribute to model predictions. This project explored the potential of convolutional neural networks (CNNs) for classifying the severity of chilli thrips damage in strawberry plants in Florida and employed XAI techniques to interpret model decisions and identify symptom-relevant canopy features. Four CNN architectures, YOLOv11, EfficientNetV2, Xception, and MobileNetV3, were trained and evaluated using 2353 square RGB canopy images of different sizes (256, 480, 640 and 1024 pixels) to classify symptoms as healthy, moderate, or severe. Trade-offs between image size, model parameter count, inference speed, and accuracy were examined in determining the best-performing model. The models achieved accuracies ranging from 77% to 85% with inference times of 5.7 to 262.3 ms, demonstrating strong potential for real-time pest severity estimation. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization revealed that model attention focused on biologically relevant regions such as fruits, stems, leaf edges, leaf surfaces, and dying leaves, areas commonly affected by chilli thrips. Subsequent analysis showed that model attention spread from localized regions in healthy plants to wide diffuse regions in severe plants. This alignment between model attention and expert scouting logic suggests that CNNs internalize symptom-specific visual cues and can reliably classify pest-induced plant stress. Full article

Unmanned helicopters play a critical role in various fields including defense, disaster response, and infrastructure inspection. Military platforms such as the MQ-8C Fire Scout represent high-value assets exceeding $40 million per unit including development costs, particularly when compared to expendable multicopter drones costing approximately $500–2000 per unit. Unexpected failures of these high-value assets can lead to substantial economic losses and mission failures, making the implementation of Health and Usage Monitoring Systems (HUMS) essential. However, the scarcity of failure data in unmanned helicopters presents significant challenges for HUMS development, while the economic feasibility of investing resources comparable to manned helicopter programs remains questionable. This study presents a novel cross-scale transfer learning framework for vibration-based anomaly detection in unmanned helicopters. The framework successfully transfers knowledge from a source domain (Airbus large manned helicopter) using publicly available data to a target domain (Stanford small RC helicopter), achieving excellent anomaly detection performance without labeled target domain data. The approach consists of three key processes. First, we developed a multi-task learning transformer model achieving an F-β score of 0.963 (β = 0.3) using only Airbus vibration data. Second, we applied CORAL (Correlation Alignment) domain adaptation techniques to reduce the distribution discrepancy between source and target domains by 79.7%. Third, we developed a Control Effort Score (CES) based on control input data as a proxy labeling metric for 20 flight maneuvers in the target domain, achieving a Spearman correlation coefficient ρ of 0.903 between the CES and the Anomaly Index measured by the transfer-learned model. This represents a 95.5% improvement compared to the non-transfer learning baseline of 0.462. Full article

Background: Missed second mesiobuccal second (MB2) canals are a recognized contributor to endodontic failure, and enhanced visualization may facilitate their detection. This study evaluated the influence of magnification devices and operator experience on MB2 detection using anatomically standardized 3D-printed maxillary first molar models. Methods: Fifty-nine endodontists and endodontic residents evaluated anatomically standardized TrueTooth® 3D-printed maxillary first molars incorporating Vertucci Type II and IV configurations. Participants were assigned to naked-eye (NE), dental loupe (DL; 3.5×), or dental operating microscope (DOM) visualization. Access cavity preparation and MB2 canal scouting times were recorded, and MB2 detection was confirmed by insertion of a size-10 K-file. Use of ultrasonic tips and long-shank burs was documented. Statistical analyses included two-way ANOVA for procedural time comparisons, chi-square or Fisher’s exact tests for categorical variables, and logistic regression to evaluate factors associated with MB2 detection (α = 0.05). Results: The overall MB2 detection rate was 49.2%. Detection varied by magnification modality, with rates of 25.0% for naked-eye visualization, 45.0% for dental loupes, and 70.0% for the dental operating microscope. In multivariable analysis using a parsimonious model, DOM use was associated with higher odds of MB2 detection; however, the confidence interval included unity, indicating a borderline association. MB2 detection rates were similar between endodontists and residents (50.0% vs. 47.6%), with no statistically significant difference between groups. Ultrasonic tip use was associated with a higher frequency of scouting-related perforations but did not improve detection. Operators who successfully detected MB2 completed scouting in significantly less time. Conclusions: Under controlled, anatomically standardized laboratory conditions, visual magnification, particularly use of the dental operating microscope, was associated with greater efficiency of MB2 canal detection and shorter scouting times, beyond non-significant trends related to operator experience. Although 3D-printed models do not fully replicate the mechanical and tactile properties of natural dentin, their reproducible anatomy allows reliable assessment of operator- and device-related factors in a controlled setting. Given the simulated environment and the presence of borderline statistical associations, these findings should be interpreted cautiously and should not be directly extrapolated to clinical outcomes without further validation in clinical studies. Full article

Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters. Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version. Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions. Full article

The western corn rootworm (Diabrotica virgifera virgifera LeConte) remains one of the most damaging pests of maize across Europe, including Romania. Reliable integrated pest management relies on monitoring systems capable of capturing adult flight activity under field conditions. This study presents a comparative field evaluation of three monitoring approaches: Virgiwit yellow sticky panels (YSP), pheromone-based CSALOMON^® KLP+ traps, and the automated iScout^® digital monitoring system. Monitoring was conducted at weekly intervals over an eight-week period (20 July–15 September 2025) in four maize fields in western Romania. Capture data were analyzed descriptively to assess relative trap performance and to explore associations with selected meteorological variables. KLP+ traps consistently recorded the highest numbers of adults, while YSP traps reproduced the main seasonal flight patterns. The iScout^® system captured fewer individuals but provided continuous temporal information on adult activity. Correlation analyses indicated generally weak and inconsistent relationships between trap captures and short-term weather variables, reflecting the limitations imposed by weekly manual sampling and site-specific variability. Overall, the results highlight the complementary strengths and limitations of manual and automated monitoring tools and support their exploratory use for characterizing seasonal flight activity and temporal population patterns of Diabrotica virgifera virgifera under field conditions. Further multi-year and device-specific validation is required before automated systems can be fully integrated into operational pest management frameworks. Full article

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring. Full article

Peer role models are an important factor in supporting academic achievement, social development, and mental health, particularly in out-of-school-time (OST) programs that emphasize character and leadership. This mixed-methods study explored whether Scouts’ racial/ethnic identity was associated with identifying a peer role model and examined the character assets youth valued in those role models. We purposively sampled 104 Scouts (aged 11–18), 89% male and 70% White, with additional racial diversity across all U.S. regions. Interviews were analyzed using both quantitative and qualitative approaches. Most Scouts identified a peer role model, and there were no significant differences in identification or valued characteristics across racial/ethnic groups. Scouts most frequently valued character assets related to caring, contribution, and connection. These findings point to the value of structured, youth-led, multi-age OST environments, indicating that program policies which embed opportunities for peer role modeling may help promote character development across diverse backgrounds. Full article

In the manufacturing of iron core for high-power transformers, a cutting stock problem arises where large-width silicon steel coils must be cut into narrower coils, known as strips. Typically, the required length of each strip far exceeds that of a single coil. Therefore, the problem necessitates additional consideration of how to split the strips and arrange them on the large coils, with the goal of minimizing the total number of strips. In this paper, we propose a discrete artificial bee colony algorithm to address this problem. The algorithm replaces the stochastic roulette wheel with biased selection in the onlooker bee phase and introduces partially mapped crossover in both the onlooker and scout bee phases. These enhancements facilitate more effective utilization of information from high-quality solutions, thereby improving the algorithm’s stability and its capacity to obtain higher-quality results. Experimental results show that compared to existing methods reported in the literature, the proposed approach reduces the total number of strips by an average of over 3.9% and 7.6% for Set 2 and Set 3, respectively, while also exhibiting a faster convergence rate than other competitive algorithms. Full article

Background: Preoperative localization of non-palpable breast lesions is critical for accurate resection and margin control in breast-conserving surgery. Traditional methods, such as wire or radioguided localization, have limitations in terms of logistics, patient comfort, and procedural flexibility. SCOUT^® is a wireless, radar-based alternative that may improve surgical precision and workflow. This study aimed to evaluate the clinical performance of the SCOUT^® in the localization of non-palpable breast and axillary lesions, including detection success, margin status, reoperation rates, and device-related events. Methods: We conducted a retrospective, single-centre observational study including 427 patients who underwent breast-conserving surgery after preoperative localization using the SCOUT^® between January 2023 and May 2024 at a tertiary academic hospital. Variables included lesion type, location, neoadjuvant treatment, device detection, seed deactivation, MRI interference, margin status, and reoperation rate. Results: The mean age was 58 ± 12.7 years, with malignant pathology in 88.5% of cases. SCOUT^® achieved a 100% detection rate in axillary localizations and 98.1% in breast lesions. Seed deactivation occurred in 1.2% of cases, all successfully managed intraoperatively. MRI artefacts were observed in 1.6% of patients, without diagnostic interference. Positive margins were reported in 8.3% of cases, representing an improvement compared with the institution’s historic 12% rate, with 5.9% requiring reoperation. Carcinoma in situ showed the highest rate of positive margins, at 26%. Conclusions: SCOUT^® was associated with high detection rates, a low incidence of device-related events, and favourable margin outcomes, supporting its reliability for the localization of non-palpable breast lesions. Full article