Search Results (473)

The advent of multichannel ground-penetrating radar systems capable of acquiring multiview, multistatic, and multifrequency data is offering new possibilities to improve subsurface imaging performance. However, this raises the need for reconstruction approaches capable of handling such sophisticated configurations and the resulting increase in the data volume. Therefore, the challenge lies in identifying proper measurement configurations that balance image quality with the complexity and duration of data acquisition. As a contribution to this topic, the present paper focuses on a measurement system working in reflection mode and composed of an array of antennas, consisting of a transmitting antenna and several receiving antennas, whose spatial offset is comparable to the probing wavelength. Therefore, for each position of the transmitting antenna, a single-view/multistatic configuration is considered. The imaging task is solved by adopting a linear microwave tomographic approach, which provides a qualitative reconstruction of the investigated scenario. In particular, a 3D inverse scattering problem is tackled for an isotropic, homogeneous, lossless, and non-magnetic medium under the Born approximation, considering both single- and multi-frequency data. A preliminary analysis, referring to a 3D free-space reference scenario, is performed in terms of the spectral content of the scattering operator and the system’s point spread function. Finally, an experimental validation under laboratory conditions is presented in order to verify the expected imaging capability of the inversion approach. Full article

In team collaboration environments, ensuring fair reward distribution is crucial for maintaining motivation and productivity. However, existing reward allocation methods often suffer from biases in self-assessment, leading to inequitable outcomes. In this study, we introduce a ranking mechanism that converts self-assessed contribution ratios into task orders based on the values of these ratios. Then we propose two methods using this mechanism: Method 1 uses quadratic optimization to adjust the contribution ratios, aligning them more closely with actual values, while Method 2 incorporates task reward differences to ensure fairer reward allotment. Experimental results show that the reward allotment method in the latest research reduces the loss by 25.31% compared to the conventional method, while our methods achieve a loss reduction of 53.28% for Method 1 and 64.4% for Method 2. Sensitivity analysis confirms the effectiveness of both methods under varying self-assessment errors, reward amounts, and task sizes, maintaining an average loss reduction of over 30%. These findings provide valuable insights for optimizing reward distribution, such as enhancing self-assessment accuracy and adjusting employee task assignments for improved fairness. Full article

Accurate prediction of geopolymer concrete compressive strength is vital for sustainable construction. Traditional experiments are time-consuming and costly; therefore, computer-aided systems enable rapid and accurate estimation. This study evaluates three ensemble learning algorithms (Extreme Gradient Boosting (XGB), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), as well as two baseline models (Support Vector Regression (SVR) and Artificial Neural Network (ANN)), for this task. To improve performance, hyperparameter tuning was conducted using Bayesian Optimization (BO). Model accuracy was measured using R², RMSE, MAE, and MAPE. The results demonstrate that the XGB model outperforms others under both default and optimized settings. In particular, the XGB-BO model achieved high accuracy, with RMSE of 0.3100 ± 0.0616 and R² of 0.9997 ± 0.0001. Furthermore, Shapley Additive Explanations (SHAP) analysis was used to interpret the decision-making of the XGB model. SHAP results revealed the most influential features for compressive strength of geopolymer concrete were, in order, coarse aggregate, curing time, and NaOH molar concentration. The graphical user interface (GUI) developed for compressive strength prediction demonstrates the practical potential of this research. It contributes to integrating the approach into construction practices. This study highlights the effectiveness of explainable machine learning in understanding complex material behaviors and emphasizes the importance of model optimization for making sustainable and accurate engineering predictions. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article

Critical thinking, as a crucial component of 21st-century core competencies, poses significant challenges for effective assessment in educational evaluation. This study proposes an automated assessment method for critical thinking in middle school Chinese language based on a Bidirectional Encoder Representations from Transformers—Convolutional Neural Network (BERT-CNN) hybrid model, achieving a multi-dimensional quantitative assessment of students’ critical thinking performance in writing through the synergistic effect of deep semantic encoding and local feature extraction. The research constructs an annotated dataset containing 4827 argumentative essays from three middle school grades, employing expert scoring across nine dimensions of the Paul–Elder framework, and designs three types of logic chain extraction algorithms: argument–evidence mapping, causal reasoning chains, and rebuttal–support structures. Experimental results demonstrate that the BERT-CNN hybrid model achieves a Pearson correlation coefficient of 0.872 in overall assessment tasks and an average F1 score of 0.770 in logic chain recognition tasks, outperforming the traditional baseline methods tested in our experiments. Ablation experiments confirm the hierarchical contributions of semantic features (31.2%), syntactic features (24.1%), and logical markers (18.9%), while revealing the model’s limitations in assessing higher-order cognitive dimensions. The findings provide a feasible technical solution for the intelligent assessment of critical thinking, offering significant theoretical value and practical implications for advancing educational evaluation reform and personalized instruction. Full article

Automated food item recognition and recipe recommendation systems have gained increasing importance in dietary management and culinary applications. Recent advancements in Computer Vision, particularly in object detection, classification, and image segmentation, have facilitated progress in these areas. However, many existing systems remain inefficient and lack seamless integration, resulting in limited solutions capable of both identifying food items and providing relevant recipe recommendations. Furthermore, modern neural network architectures have yet to be extensively applied to food recognition and recipe recommendation tasks. This study aims to address these limitations by developing a system based on the MobileNetV2 architecture for accurate food item recognition, paired with a recipe recommendation module. The system was trained on a diverse dataset of fruits and vegetables, achieving high classification accuracy (97.2%) and demonstrating robustness under various conditions. Our findings indicate that the modified model, the MobileNetV2 model, can effectively recognize different food items, making it suitable for real-time applications. The significance of this research lies in its potential to improve dietary tracking, offer valuable culinary insights, and serve as a practical tool for both personal and professional use. Ultimately, this work advances food recognition technology, contributing to enhanced health management and fostering culinary creativity. Some potential applications of this work include personalized dietary management, automated meal logging for fitness apps, smart kitchen assistants, restaurant ordering systems, and enhanced food analysis for nutrition tracking. Full article

Implicit learning describes learning from experience that is not available to conscious awareness. The question of whether some individuals are better implicit learners than others has suggested and may contribute to difference in performance among experts. Across four experiments, adult participants completed the Serial Interception Sequence Learning (SISL) task across multiple parallel learning assessment forms. Previously, SISL sequence-specific performance has been shown to resist explicit knowledge influence, allowing for repeated reassessments of implicit learning with novel statistical structure. Our findings indicate that group-level sequence-specific performance occurred robustly in each reassessment; however, participants who exhibited more sequence-specific performance on one assessment did not exhibit better performance on parallel assessments, indicating no rank-order stability in learning. In all four experiments, with two to twelve reassessments of learning, no participants exhibited consistently better sequence learning rates than the other participants, indicating no evidence for a better ability in implicit learning. Measurements of other cognitive constructs, such as processing speed collected in parallel, exhibited robust individual differences. In Experiment 4, a general battery of cognitive measurements showed typical individual differences in measures of working memory, processing speed, and personality, but none correlated with implicit learning ability. We hypothesize that implicit learning arises from a general process of neuroplasticity reorganizing functions during practice and that our findings suggest that this process occurs at a basically similar rate across all people. Everybody learns from practice implicitly, but results suggest that the learning rate does not vary substantially across this sample. Full article

The present study examines the application of interactive software models for training on the topic of “Cyclic Codes” in order to increase the success rate and engagement of students in technical disciplines. Two models have been developed—based on the polynomial method and the LFSR approach—through an established methodology adapted to the specifics of the content. A pedagogical experiment with a control and experimental group was conducted, and ANCOVA analysis was applied to eliminate the influence of initial grades. The results show a statistically significant advantage of the experimental group in terms of final grades, which confirms the positive effect of using interactive models. The analysis of engagement and solved tasks reveals that the polynomial model is used more widely and contributes to the systematic application of algorithmic steps, while the LFSR model has an illustrative nature and supports intuitive understanding through visualization of processes. The feedback received from students shows high satisfaction and points to improvements in the interface and functionality. In conclusion, interactive models prove their effectiveness as complementary tools for learning complex technical concepts, and prospects for future development through the integration of artificial intelligence and enhanced gamification are also discussed. Full article

Speckle noise inherent in Synthetic Aperture Radar (SAR) images severely degrades image quality and hinders downstream tasks such as interpretation and target recognition. Existing despeckling methods, both traditional and deep learning-based, often struggle to balance effective speckle suppression with structural detail preservation. Although Denoising Diffusion Probabilistic Models (DDPMs) have shown remarkable potential for SAR despeckling, their computational overhead from iterative sampling severely limits practical applicability. To mitigate these challenges, this paper proposes the Efficient Conditional Diffusion Model (ECDM) for SAR despeckling. We integrate the cosine noise schedule with a joint variance prediction mechanism, accelerating the inference speed by an order of magnitude while maintaining high denoising quality. Furthermore, we integrate wavelet transforms into the encoder’s downsampling path, enabling adaptive feature fusion across frequency bands to enhance structural fidelity. Experimental results demonstrate that, compared to a baseline diffusion model, our proposed method achieves an approximately 20-fold acceleration in inference and obtains significant improvements in key objective metrics. This work contributes to real-time processing of diffusion models for SAR image enhancement, supporting practical deployment by mitigating prolonged inference in traditional diffusion models through efficient stochastic sampling. Full article

We developed and validated an animation-based assessment (ABA) method for evaluating high school students’ inquiry competencies in Taiwan’s 12-Year Curriculum. Contextualized in atmospheric chemistry involving methane and hydroxyl radicals, ABA integrated dynamic simulations, tiered multiple-choice and open-ended tasks, and process tracking on the CloudClassRoom platform, the assessment focused on measuring two inquiry skills: causal reasoning and critical thinking. The results of 26,823 students revealed that the ABA effectively differentiated student performance across ability levels and academic disciplines, with open-ended items sensitive to higher-order reasoning. Gender difference was not observed, indicating the gender-free design of the developed ABA. While the ABA supports diagnostic insights, limitations need to be addressed, including the underassessment of modeling and creative experimentation skills. Therefore, it is necessary to include open modeling tasks and AI-powered semantic scoring. The developed ABA contributes a scalable, competency-aligned framework for inquiry-based science assessments. Full article

This work formulates and solves the actual problem of studying the logistics of unmanned aerial vehicle (UAV) operations in facility security planning. The study is related to security tasks, including perimeter control, infrastructure condition monitoring, prevention of unauthorized access, and analysis of potential threats. Thus, the topic of the proposed publication is relevant as it examines the sequence of logistical actions in the large-scale application of a swarm of drones for facility protection. The purpose of the research is to create a set of mathematical and simulation models that can be used to analyze the capabilities of a drone swarm when organizing security measures. The article analyzes modern problems of using a drone swarm: formation of the swarm, assessment of its potential capabilities, organization of patrols, development of monitoring scenarios, planning of drone routes and assessment of the effectiveness of the security system. Special attention is paid to the possibilities of wave patrols to provide continuous surveillance of the object. In order to form a drone swarm and possibly divide it into groups sent to different surveillance zones, the necessary UAV capacity to effectively perform security tasks is assessed. Possible security scenarios using drone waves are developed as follows: single patrolling with limited resources; two-wave patrolling; and multi-stage patrolling for complete coverage of the protected area with the required number of UAVs. To select priority monitoring areas, the functional potential of drones and current risks are taken into account. An optimization model of rational distribution of drones into groups to ensure effective control of the protected area is created. Possible variants of drone group formation are analyzed as follows: allocation of one priority surveillance zone, formation of a set of key zones, or even distribution of swarm resources along the entire perimeter. Possible scenarios for dividing the drone swarm in flight are developed as follows: dividing the swarm into groups at the launch stage, dividing the swarm at a given navigation point on the route, and repeatedly dividing the swarm at different patrol points. An original algorithm for the formation of drone flight routes for object surveillance based on the simulation modeling of the movement of virtual objects simulating drones has been developed. An agent-based model on the AnyLogic platform was created to study the logistics of security operations. The scientific novelty of the study is related to the actual task of forming possible strategies for using a swarm of drones to provide integrated security of objects, which contributes to improving the efficiency of security and monitoring systems. The results of the study can be used by specialists in security, logistics, infrastructure monitoring and other areas related to the use of drone swarms for effective control and protection of facilities. Full article

Background/Objectives: According to Embodied Cognition theories, motor skills in early childhood are closely interconnected with various cognitive abilities, including working memory, cognitive flexibility, and theory of mind. These processes are integral components of the multicomponent model of narrative comprehension, which posits that higher-order cognitive functions support the construction of coherent mental representations of narrative meaning. This study aimed to examine whether motor skills directly contribute to narrative comprehension in preschool children or whether this relationship is mediated by cognitive skills. Methods: Seventy-four typically developing children aged 3 to 6 years (47.2% female) participated in this study. Motor skills were assessed using standardized measures, and cognitive abilities were evaluated through tasks targeting working memory, cognitive flexibility, and theory of mind. Narrative comprehension was measured with age-appropriate tasks requiring the understanding and retelling of stories. A structural equation model (SEM) was conducted to test the direct and indirect effects of motor skills on narrative comprehension via cognitive skills. Results: The SEM results indicated a significant direct effect of motor skills on cognitive skills and an indirect effect on narrative comprehension mediated by cognitive abilities. No evidence was found for a direct pathway from motor skills to narrative comprehension independent of cognitive processes. Conclusions: These findings underscore the complex interplay between motor, cognitive, and language development in early childhood. The results suggest that motor skills contribute to narrative comprehension indirectly by enhancing core cognitive abilities, offering novel insights into the developmental mechanisms that support language acquisition and understanding. Full article

Generalising deep-learning models to perform well on unseen data domains with minimal retraining remains a significant challenge in computer vision. Even when the target task—such as quantifying the number of elements in an image—stays the same, data quality, shape, or form variations can deviate from the training conditions, often necessitating manual intervention. As a real-world industry problem, we aim to automate stock level estimation in retail cabinets. As technology advances, new cabinet models with varying shapes emerge alongside new camera types. This evolving scenario poses a substantial obstacle to deploying long-term, scalable solutions. To surmount the challenge of generalising to new cabinet models and cameras with minimal amounts of sample images, this research introduces a new solution. This paper proposes a novel ensemble model that combines DenseNet-201 and Vision Transformer (ViT-B/8) architectures to achieve generalisation in stock-level classification. The novelty aspect of our solution comes from the fact that we combine a transformer with a DenseNet model in order to capture both the local, hierarchical details and the long-range dependencies within the images, improving generalisation accuracy with less data. Key contributions include (i) a novel DenseNet-201 + ViT-B/8 feature-level fusion, (ii) an adaptation workflow that needs only two images per class, (iii) a balanced layer-unfreezing schedule, (iv) a publicly described domain-shift benchmark, and (v) a 47 pp accuracy gain over four standard few-shot baselines. Our approach leverages fine-tuning techniques to adapt two pre-trained models to the new retail cabinets (i.e., standing or horizontal) and camera types using only two images per class. Experimental results demonstrate that our method achieves high accuracy rates of 91% on new cabinets with the same camera and 89% on new cabinets with different cameras, significantly outperforming standard few-shot learning methods. Full article

This paper presents a new methodology for the design process of DC ripple filters for voltage source converters. It focuses on fast-switching, wide-bandgap-material-based converters. Therefore, a wide frequency range of up to 100 MHz is taken into consideration during the whole process. Different tools like analytic calculations, time-domain modelling, and the finite element method are used for different tasks in order to generate a realistic model in terms of filter effect and reliability. The models are validated by small-signal measurements using a vector network analyser as well as realistic high-power tests. The contribution of this paper is to provide a tool for DC link filter design to estimate the filter efficiency and the current stress on the filter elements with a special focus on WBG hardware. Full article

In this article, the application of virtual reality technology for the realistic and immersive visualization of various tasks and scenarios in fields such as power engineering and fire safety has been examined in order to help prepare students and professional electrical engineers with electrical safety, the operation of electrical substations, potential emergencies, injury prevention, fire safety, and others. Additionally, the use of machine learning algorithms to guide evacuations from hazardous environments, fault prevention, fire prediction, and discovery of conductive materials has been examined. The most frequently used algorithms in these areas have also been described and summarized, and conclusions have been made about the combined advantages of using VR and ML algorithms. Finally, the needs, contributions, and challenges of using machine learning in virtual reality projects have been examined. Full article