Search Results (529)

The process of manually scoring drawings for the Goodenough–Harris Draw-a-Person (DAP) test is time-consuming and labor-intensive. It is also prone to inconsistencies due to subjective interpretation. Keeping these drawbacks in mind, this study aims to introduce a hybrid model of automated analysis and scoring of DAP test results using a combination of deep learning and rule-based reasoning. The proposed model has two different modules: one for predicting ten visual anatomical features of drawings using a convolutional neural network (CNN), and another set of six rules for representing geometric and spatial relationships. The output of the CNN is converted to binary using thresholding and then concatenated with the results of heuristic rules to obtain a final set of sixteen features. The proposed model was also evaluated using five-fold cross-validation methods and a separate hold-out test set containing 948 labeled drawings. The evaluation using the five-fold cross-validation approach shows that the proposed approach maintains consistent performance with high average F1-scores for all primary anatomical features above 0.90. On the other hand, the evaluation using the hold-out test set revealed that the proposed approach achieved a high macro-average accuracy of 91.78% for all sixteen features. This implies that the proposed approach has a high degree of generalization capability for the problem domain. The proposed approach achieves almost-perfect scores for structurally prominent anatomical features such as the head, limbs, trunk-related relationships, and all heuristic-based features. Nevertheless, the proposed approach performs poorly for less visually distinguishable anatomical features such as the ears (average F1-scores ≈ 0.09–0.12) and the neck (average F1-scores ≈ 0.75). The evaluation results show that the proposed approach is efficient in approximating expert-level scoring with a considerable reduction in human effort. Nevertheless, some limitations exist in the proposed approach. First, the proposed approach is less robust for subtle anatomical features. Second, the proposed approach relies on heuristic thresholds for feature extraction. Third, the proposed approach equally weighs all sixteen features; however, this may not exactly match the actual DAP scoring system. Full article

Frontal alpha asymmetry (FAA), a pattern of brain activity that reflects the difference in alpha wave power between the left and right frontal areas of the brain, is considered a stable marker for an individual’s tendency to experience either more approach-related or withdrawal-related emotions. On the other hand, electrodermal activity (EDA) measures arousal by tracking changes in skin sweat, which are controlled by the sympathetic nervous system. This study explores the interrelation between EDA features, obtained from time and frequency domains, with FAA by means of the mutual information. Multiple cognitive tasks such as EAT, ship search, PVT and N-Back were analyzed in 10 participants in intervals of two hours over 24 h (12 trials), in which they had to face sleep deprivation conditions. The most informative EDA features about FAA, were used to identify the two main clusters associated to high and low FAA values through the hierarchical agglomerative clustering approach. Once data is labeled, a supervised classifier based on support vector machines (SVMs) is used to identify positive and negative emotional states by using a rigorous one-trial out cross-validation scheme. Results show consistent performance within tasks and trials, achieving accuracy values over 80% on average, giving an important insight about the use of EDA signal as an alternative to the more complex FAA measurement for tracking positive or negative emotional states. Full article

Magnetic resonance imaging (MRI) inherently suffers from motion artifacts and inter-slice misalignment, primarily due to sequential slice acquisition and the prolonged scanning time required for dynamic cardiac motion. These acquisition-induced inconsistencies often lead to anatomically implausible representations of cardiac structures, impairing subsequent clinical analyses such as 3D reconstruction and regional functional assessment. On the other hand, acquiring high-resolution MRI demands extended scan durations that increase patient burden and potential health risks. To address this challenge, we propose a deep motion correction and super-resolution whole-heart reconstruction (DeepWHR) framework. It learns cardiac structure prior knowledge from computed tomography (CT) data, and transfers it to reconstruct cardiac structure from conventional misaligned and large slice thickness MRI images. Specifically, DeepWHR utilizes CT anatomy data to train a deep motion correction model that enables the network to capture structurally coherent and anatomically consistent representations, while MRI Finetune preserves modality-specific spatial characteristics, ensuring that the reconstructed results retain the intrinsic MRI data distribution. Furthermore, DeepWHR introduced an implicit neural representation module, which models continuous spatial fields, enabling multi-scale super-resolution structure reconstruction. Experiments on the CARE2024 WHS dataset validate that our method not only restores the spatial coherence of MRI-derived anatomical structures but also generates high-fidelity label representations suitable for downstream cardiac applications. This study demonstrates that DeepWHR transforms sparse, misaligned 2D label stacks into anatomically coherent, high-resolution 3D models, enhancing their reliability for clinical applications. Full article

This paper examines the use of Machine Learning (ML) approaches in maintaining academic integrity using the information provided in the Moodle system logs. The paper focuses on data set construction, handling the issue of class imbalance, and the assessment of the performance of different ML models in uncovering academic fraud. Twelve different data sets were created by using the concept of temporal windows (e.g., one-day and three-day windows) during the feature extraction stage from the Moodle system logs. The manual labeling of the data sets was done based on a predefined set of rules that outline the fraudulent activities. The issue of class imbalance was treated using eleven different resampling approaches, such as SMOTE, ADASYN, Tomek Links, and NearMiss. We evaluated six classification algorithms, thus resulting in a total of 792 experiments based on the interactions between the data sets, resampling methods, and classification algorithms. The results from the experiment show that the Random Forest and AdaBoost models performed the best in the experiment. Furthermore, we observed a trade-off between fraud detection rates and model precision based on the temporal windows and resampling methods. The shortest temporal windows and hybrid undersampling approaches resulted in the maximum recall value in this study and could identify the greatest number of at-risk students. On the other hand, the longest temporal windows and hybrid oversampling approaches with data cleaning resulted in the best results in terms of F1-Score and Cohen’s Kappa statistics. The results provide conclusive evidence that the models can identify fraud; however, they should be used as predictive models for the improvement of proctoring approaches, such as random selection for verification or seating arrangement strategies, instead of judgment models. Full article

Routine evaluation of insulator performance is important for maintaining the reliability and safety of power system operations. The use of unmanned aerial vehicles (UAVs) has been a significant advancement in transmission line monitoring, effectively replacing traditional manual inspection methods. With the rapid advancement of deep learning techniques, methods based on these models for detecting insulator defects have attracted increasing research interest and achieved notable advancements. Nevertheless, existing approaches primarily emphasize constructing sophisticated and intricate network architectures, which consequently lead to greater inference complexity when applied in practical scenarios. On the other hand, foggy scenarios pose challenges for learning algorithms due to difficulties in obtaining and labeling samples, as well as the poor performance of detectors trained on clear-weather samples. This study proposes adaptive enhancement based on YOLO, a framework that has robustness and domain generalization under fog-induced distribution shifts. It optimizes at multiple scales and enhances images as input to a detector in a single pipeline. Experimental results demonstrate improved performance on public UPID and SFID insulator defect datasets, improving insulator defect detection precision without increased computational complexity or inference resources, which is of great significance for advancing object detection in adverse weather. The proposed method achieves real-time performance, with an end-to-end inference speed exceeding 25 FPS and a model-only speed of approximately 38 FPS on 678 images from UPID, demonstrating both practical applicability and computational efficiency. Full article

Background: Deep brain stimulation (DBS) has traditionally followed diagnosis-driven, nucleus-centered targeting paradigms. Increasing evidence supports a circuit-based framework in which clinical outcomes depend on modulation of symptom-relevant networks rather than diagnostic labels alone. This approach is particularly relevant in mixed movement disorder phenotypes such as dystonic tremor, where the most disabling symptom may not align with the conventional surgical target. Methods: We report a clinically illustrative single case treated using a symptom-oriented, connectome-informed DBS strategy. Clinical phenotype, tremor severity, functional impairment, prior medical and botulinum toxin treatments, and longitudinal outcomes were systematically reviewed. DBS target selection prioritized the dominant, treatment-refractory symptom rather than the underlying dystonia diagnosis. Surgical planning incorporated high-resolution MRI with patient-specific thalamic segmentation using Brainlab Brain Elements^®, followed by postoperative lead localization and volume of tissue activated visualization with the SureTune™ platform. Results: A 54-year-old left-handed woman with long-standing cervical dystonia developed a severe, markedly asymmetric dystonic tremor predominantly affecting the left upper limb, resulting in profound functional disability. Instead of conventional bilateral globus pallidus internus DBS, unilateral right ventral intermediate nucleus (VIM) DBS was selected to engage tremor-related cerebellothalamic circuits. Rapid and marked improvement was observed, with tremor severity reduced to mild levels within 15 days after stimulation onset. At 6-month follow-up, overall tremor severity improved from 49 to 13 points on the Fahn–Tolosa–Marin Tremor Rating Scale, corresponding to a 73.5% reduction. This improvement was associated with restoration of legible handwriting, independent feeding and drinking, and recovery of bimanual fine motor function. Clinical benefit remained stable throughout follow-up, without stimulation-related adverse effects. Conclusions: This case illustrates the feasibility of a symptom-oriented, connectome-informed DBS strategy in selected patients with dystonic tremor. When symptom expression and network involvement are markedly asymmetric, selective unilateral modulation of the tremor-dominant circuit may achieve meaningful and durable functional improvement. Further studies are needed to assess the generalizability of this approach. Full article

Agent-based models of financial markets often rely on a small set of hand-crafted trading rules, making it difficult to relate model heterogeneity to information that is observable in market data. We take a different standpoint and treat the design of heterogeneity as a representation problem under limited observations. In our framework, each agent’s decision rule is implemented as a neural-network mapping from recent price histories to order decisions, trained on historical index or stock price series. To describe and manipulate heterogeneity without pre-assigning mechanism labels, we introduce Fit Quality (FQ), an ex post effect-defined index summarizing how strongly each learned rule fits the price patterns it was trained on, and we use FQ solely as a coordinate for organizing agent populations and constructing controlled changes in agent composition, rather than as a measure of forecasting skill or economic performance. Using this representation, we examine whether simulations can reproduce several stylized features of return series. We also perform simple ablation experiments to assess how far the observed properties depend on the data-trained decision rules rather than on the market mechanism alone. Taken together, the framework is intended as a step toward more data-linked, representation-conscious agent-based models, in which alternative ways of organizing heterogeneity can be compared within a common market environment. Full article

RGB-infrared (RGB-IR) object detection leverages complementary information from these two modalities to substantially enhance perception in complex environments, which is particularly beneficial for reliable detection under adverse imaging conditions such as low illumination and severe haze. However, RGB-IR object detection still faces several challenges due to pronounced intra-modality and cross-modality discrepancies. On the one hand, many existing approaches rely on complex architectures to strengthen cross-modal interactions, which increases computational cost. On the other hand, symmetric dual-branch backbones with a static fusion paradigm often struggle to explicitly characterize discrepancies between the RGB and IR modalities. This limitation prevents effective mining of complementary information and reduces the discriminability of fused representations. To address these issues, this paper presents a lightweight RGB-IR multimodal detection network (LMDENet), which consists of three key components: (1) an illumination-guided label selection (IGLS) that integrates RGB and IR labels based on cross-modal matching and illumination-aware rules to construct consistent and reliable supervision; (2) a heterogeneous backbone network (HBN) with differentiated branches that separately model RGB appearance details and IR structural information, improving modality-specific representation learning; and (3) a difference-complement enhancement module (DCEM) that explicitly decomposes cross-modal features into common and difference components and performs selective enhancement to amplify complementary information while suppressing redundant noise. We systematically evaluate the detection performance of the proposed model on the multimodal remote sensing dataset DroneVehicle, and further conduct supplementary experiments on the LLVIP dataset to verify its generalization ability across different scenarios. Experimental results on the DroneVehicle and LLVIP datasets demonstrate that LMDENet achieves $78.9\%$ and $93.6\%$ mAP@0.5, respectively. Meanwhile, the model contains only 3.3 M parameters and 8.7 G FLOPs, reflecting a favorable accuracy–efficiency balance. Full article

Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL) has emerged as a promising research direction. Few-shot learning facilitates the expedited adaptation to novel tasks utilizing a limited number of labeled samples, whereas incremental learning concentrates on the continuous refinement of the model as new categories are incorporated without eradicating previously learned knowledge. Although both methodologies present potential resolutions to the challenges of sample scarcity and class evolution in SAR target recognition, they are not without their own set of difficulties. Fine-tuning with emerging classes can perturb the feature distribution of established classes, culminating in catastrophic forgetting, while training exclusively on a handful of new samples can induce bias towards older classes, leading to distribution collapse and overfitting. To surmount these limitations and satisfy practical application requirements, we propose a Few-Shot Class-Incremental SAR Target Recognition method based on a Dynamic Task-Adaptive Classifier (DTAC). This approach underscores task adaptability through a feature extraction module, a task information encoding module, and a classifier generation module. The feature extraction module discerns both target-specific and task-specific characteristics, while the task information encoding module modulates the network parameters of the classifier generation module based on pertinent task information, thereby improving adaptability. Our innovative classifier generation module, honed with task-specific insights, dynamically assembles classifiers tailored to the current task, effectively accommodating a variety of scenarios and novel class samples. Our extensive experiments on SAR datasets demonstrate that our proposed method generally outperforms the baselines in few-shot class incremental SAR target recognition. Full article

Background/Objectives: Prostate cancer is the most commonly diagnosed cancer in men and a leading cause of cancer-related mortality, highlighting the need for delivery systems capable of efficiently transporting both chemotherapeutic drugs and therapeutic genes to tumor cells. Generation-3 diaminobutyric polypropylenimine (DAB) dendrimers display low toxicity, high drug loading capacity and efficient gene delivery, and can be engineered as camptothecin-bearing PEGylated carriers complexed with plasmid DNA. The aim of this study was to compare microfluidic processing with conventional hand mixing for the preparation of camptothecin-bearing PEGylated DAB dendriplexes and to evaluate the impact of formulation methods and microfluidic parameters on their physicochemical properties, cellular uptake and gene expression in prostate cancer cells. Methods: Camptothecin-bearing PEGylated DAB dendrimers were synthesized and complexed with plasmid DNA to form dendriplexes. Formulations were prepared either by microfluidics, using different total flow rates and aqueous: organic flow rate ratios, or by conventional hand mixing. The resulting dendriplexes were characterized for DNA condensation, particle size, polydispersity index and zeta potential. Morphology was assessed by transmission electron microscopy. Cellular uptake of fluorescein-labelled DNA and β-galactosidase reporter gene expression were evaluated in PC3-Luc and DU145 prostate cancer cells. Results: Both microfluidic and hand-mixed methods produced stable, nanosized, positively charged dendriplexes with efficient and sustained DNA condensation (more than 99% over 24 h). Microfluidic processing, particularly at an aqueous: organic flow rate ratio of 3:1, yielded dendriplexes with hydrodynamic diameters and zeta potentials comparable to or slightly improved over hand-mixed formulations. These microfluidic conditions significantly enhanced cellular uptake in both PC3-Luc and DU145 cells. In PC3-Luc cells, this translated into β-galactosidase expression levels comparable to hand-mixed dendriplexes and higher than naked DNA, whereas in DU145 cells, transfection efficiencies remained modest for all formulations despite increased uptake. Conclusions: Microfluidic processing enables the reproducible and scalable preparation of camptothecin-bearing PEGylated DAB dendriplexes with tunable physicochemical properties. Under selected conditions, in vitro cellular uptake and gene expression were comparable to conventional hand mixing, supporting microfluidics as a robust alternative platform for the manufacture of dendrimer-based systems for combined chemo–gene delivery in prostate cancer. Full article

Music conveys emotion through a complex interplay of structural and acoustic cues, yet how these features map onto specific affective interpretations remains a key question in music cognition. This study explored how listeners, unaware of contextual information, categorized 110 emotionally diverse excerpts—varying in key, tempo, note density, acoustic energy, and expressive gestures—from works by Bach, Beethoven, and Chopin. Twenty classically trained participants labeled each excerpt using six predefined emotional categories. Emotion judgments were analyzed within a supervised multi-class classification framework, allowing systematic quantification of recognition accuracy, misclassification patterns, and category reliability. Behavioral responses were consistently above chance, indicating shared decoding strategies. Quantitative analyses of live performance recordings revealed systematic links between expressive features and emotional tone: high-arousal emotions showed increased acoustic intensity, faster gestures, and dominant right-hand activity, while low-arousal states involved softer dynamics and more left-hand involvement. Major-key excerpts were commonly associated with positive emotions—“Peacefulness” with slow tempos and low intensity, “Joy” with fast, energetic playing. Minor-key excerpts were linked to negative/ambivalent emotions, aligning with prior research on the emotional complexity of minor modality. Within the minor mode, a gradient of arousal emerged, from “Melancholy” to “Power,” the latter marked by heightened motor activity and sonic force. Results support an embodied view of musical emotion, where expressive meaning emerges through dynamic motor-acoustic patterns that transcend stylistic and cultural boundaries. Full article

This protocol details an optimized step-by-step procedure for performing the Seahorse XF Cell Mito Stress Test on human umbilical vein endothelial cells (HUVECs) using the Agilent Seahorse XF Pro Analyzer. Designed to address practical challenges often overlooked in standard manuals, the method preserves the native adherent state of HUVECs—a key in vitro model in vascular aging (VA) research—enabling real-time, label-free measurement of mitochondrial respiration and glycolytic function without cell detachment. The workflow is presented chronologically, covering instrument preparation, cell seeding, compound loading, assay execution, and post-assay normalization, with integrated notes and troubleshooting tips refined through hands-on experience based on the official manuals. This protocol aims to set up a detailed, rearranged standard workflow to improve experimental efficiency, reduce operator error, and support reproducible and well-organized metabolic profiling of HUVECs in aging and cardiovascular studies. Full article

With the ongoing miniaturization of smart devices, fine-grained hand gesture recognition using millimeter-wave radar has attracted increasing attention, yet practical deployment remains challenging in continuous-gesture segmentation, robust feature extraction, and reliable classification. This paper presents an end-to-end fine-grained gesture recognition framework based on frequency modulated continuous wave(FMCW) millimeter-wave radar, including gesture design, data acquisition, feature construction, and neural network-based classification. Ten gesture types are recorded (eight valid gestures and two return-to-neutral gestures); for classification, the two return-to-neutral gesture types are merged into a single invalid class, yielding a nine-class task. A sliding-window segmentation method is developed using short-time Fourier transformation(STFT)-based Doppler-time representations, and a dataset of 4050 labeled samples is collected. Multiple signal classification(MUSIC)-based super-resolution estimation is adopted to construct range–time and angle–time representations, and instance-wise normalization is applied to Doppler and range features to mitigate inter-individual variability without test leakage. For recognition, a variable-channel deep residual shrinkage network (DRSN) is employed to improve robustness to noise, supporting single-, dual-, and triple-channel feature inputs. Results under both subject-dependent evaluation with repeated random splits and subject-independent leave one subject out(LOSO) cross-validation show that DRSN architecture consistently outperforms the RefineNet-based baseline, and the triple-channel configuration achieves the best performance (98.88% accuracy). Overall, the variable-channel design enables flexible feature selection to meet diverse application requirements. Full article

Oxidative stress arises from an imbalance between reactive oxygen species (ROS) and antioxidant defense mechanisms and disrupts the structural integrity of macromolecules such as lipids, proteins, and DNA. This biochemical imbalance triggers the pathogenesis of cardiovascular and neurodegenerative diseases and leads to lipid oxidation and quality degradation in food systems. Plant-derived bioactive compounds (BACs) such as polyphenols and terpenes develop versatile molecular strategies to mitigate this oxidative damage. In addition to their direct radical scavenging effects, polyphenols stimulate the synthesis of endogenous antioxidant enzymes such as superoxide dismutase (SOD) and catalase (CAT) by activating the Nrf2–Keap1 signaling pathway. Terpenes, on the other hand, create a specialized protective shield in lipid-based matrices through “chain-breaking” reactions and a “slingshot” mechanism that externally halts the oxidation of γ-terpinene. In food engineering applications, these compounds meet the demand for “clean-label” products by providing alternatives to synthetic antioxidants such as BHA and BHT. Specific terpenes, such as carnosic acid, demonstrate higher performance in inhibiting lipid oxidation compared to their synthetic counterparts. Although BAC use extends the shelf life of products while maintaining color and flavor stability, potential interactions with protein digestibility necessitate dosage management. From a clinical perspective, these compounds suppress inflammatory responses by inhibiting the NF-κB pathway and contribute to the prevention of chronic diseases by modulating the gut microbiota. This review evaluates the capacity of BACs to manage oxidative stress in food preservation technologies and human health through a mechanistic and application-based approach. Full article

The use of Pleurotus ostreatus lyophilizate (POL) and hot water extract (POE) as potential functional ingredients for the development of enriched bread was investigated. The effects of POL and POE were examined based on the results of empirical rheological measurements and physical, textural and sensory analysis of bread. POL was incorporated into dough as a partial substitute for wheat flour (1% and 5%), while POE was added as a replacement for part of the water required to achieve optimal dough consistency (2% and 10%). Inclusion of POL in dough formulation caused dough disintegration: extreme decrease in degree of softening, from 60 FU (control) to 275 FU (POL1) and 290 FU (POL5), and extensographic measurements could not be performed. The specific volume of bread with 10% POL decreased by 46% and the crumb hardness increased approximately four times compared to the control. On the other hand, rheological properties of dough with POE were comparable to control, resulting in minimal impact on physical, textural and sensory characteristics of bread. Both fortifying ingredients positively affected the total phenolic content and antioxidant activity of the bread, with a more pronounced effect observed for POL compared to POE. Additionally, bread enriched with 5% POL had total dietary fiber content of 4.7 g/100 g and could be labeled as a source of fiber. P. ostreatus derivatives show great potential for functional bread development; however, further research is needed to optimize their use and maintain bread quality. Full article