Search Results (454)

In this paper, we explore the spiking encoding methodology within spiking neural networks for affective state recognition, deriving inspiration from the principles of quantum entanglement. A pioneering encoding strategy is proposed based on the strategic utilization of the quantum mechanical phenomenon of entanglement. By integrating quantum mechanisms into the spike-encoding pipeline, we aim to match the accuracy of existing encoders on emotion-classification tasks while retaining the inherently low-power advantage of spiking neural networks. Notably, leveraging the superposition of quantum bits and their potential quantum entanglement of adjacent values in feature space during encoding calculations, this quantum-inspired encoding paradigm holds substantial promise for augmenting information processing capabilities in brain-like neural networks. Through quantum observation, we derive spike trains characterized by quantum states, thereby establishing a foundation for experimental validation and subsequent investigative pursuits. We conducted experiments on emotion recognition and validated the effectiveness of our method. Full article

To achieve precise in-hand manipulation and feedback control using soft robotic fingers, it is essential to accurately measure their deformable structures. In particular, estimating the three-dimensional shape of a soft finger under contact conditions is a critical challenge, as the deformation state directly affects manipulation reliability. However, nonlinear deformations and occlusions arising from interactions with external objects make the estimation difficult. To address these issues, we propose a soft finger structure that integrates small magnets and magnetic sensors inside the body, enabling the acquisition of rich deformation information in both contact and non-contact states. The design provides a 15-dimensional time-series signal composed of motor angles, motor currents, and magnetic sensor outputs as inputs for shape estimation. Built on the sensing signals, we propose a mode-selection-based learning approach that outputs multiple candidate shapes and selects the correct one. The proposed network predicts the three-dimensional positions of four external markers attached to the finger, which serve as a proxy representation of the finger’s shape. The network is trained in a supervised manner using ground-truth marker positions measured by a motion capture system. The experimental results under both contact and non-contact conditions demonstrate that the proposed method achieves an average estimation error of approximately 4 mm, outperforming conventional one-shot regression models that output coordinates directly. The integration of magnetic sensing is demonstrated to be able to enable accurate recognition of contact states and significantly improve stability in shape estimation. Full article

Osteoarthritis (OA) is a severe and progressive joint disease that usually affects elderly people. The consequence of this disease in its advanced stage is the need for total knee arthroplasty (TKA). Over the years, there has been a constant increase in the number of TKA procedures, with a predicted increase to 1.26 million procedures by 2030. Diagnostics are based on conventional radiography, although advanced techniques such as radiostereometry, SPECT/CT and PET/CT, which enable early detection of micromigration, are gaining increasing recognition. Vibroarthrography (VAG) is a proposed supplement to diagnostics, enabling the assessment of the characteristics of vibrations and friction of joint surfaces, thus supporting the process of early detection of endoprosthesis instability. The combination of conventional and alternative diagnostic methods, including vibroarthrography, may improve the detection of early TKA loosening. This may also result in increased implant durability. The aim of this article is to review the current state of knowledge on the classification and analysis of endoprosthesis loosening mechanisms. In addition, classic and modern methods of detecting and monitoring loosening are discussed, with particular emphasis on vibroarthrography as a potential tool for early diagnosis. Full article

Background/Objectives: Executive functions (EFs) and Theory of Mind (ToM) are often compromised in children with Specific Learning Disorders (SLD). Although evidence highlights the malleability of EF, studies have yet to investigate whether school-based interventions can enhance both cool and hot EF domains and support ToM development in this population. This study evaluated the effectiveness of a structured, classroom-based EF training program in improving cool EF, hot EF, and ToM in children with SLD. Methods: Forty students with SLD (aged 8–10 years) were allocated to an intervention group (n = 24) or a passive control group (n = 16). The program was delivered in small groups during regular school hours over 6–9 weeks (18 sessions). Pre- and post-intervention assessments measured cool EFs (working memory, planning, cognitive flexibility, inhibition), hot EFs (affective decision-making, delay of gratification), and ToM (false belief understanding, mental state/emotion recognition). Results: The intervention group showed significant within-group improvements in working memory, planning, and cognitive flexibility, whereas the control group showed no significant changes. Between-group comparisons revealed significant effects for working memory, planning, and ToM mental state/emotion recognition, with medium-to-large effect sizes. No significant group differences were found for hot EFs or ToM false belief understanding. Conclusions: These findings suggest that participation in a structured, school-based EF program is associated with selective improvements in specific cool EF components and one aspect of ToM in children with SLD, supporting the potential value of classroom-based interventions for cognitive and socio-cognitive development. Full article

Facial expression recognition (FER) is crucial for affective computing and human–computer interaction; however, it is still difficult to achieve under various conditions in the real world, such as lighting, occlusion, and pose. This work presents a lightweight hybrid network, SE-Hybrid + Face-ViT, which merges convolutional and transformer architectures through multi-level feature fusion and adaptive channel attention. The network includes a convolutional stream to capture the fine-grained texture of the image and a retrained Face-ViT branch to provide the high-level semantic context. Squeeze-and-Excitation (SE) modules adjust the channel responses at different levels, thus allowing the network to focus on the emotion-salient cues and suppress the redundant features. The proposed architecture, trained and tested on the large-scale AffectNet benchmark, achieved 70.45% accuracy and 68.11% macro-F1, thereby outperforming the latest state-of-the-art models such as TBEM-Transformer, FT-CSAT, and HFE-Net by around 2–3%. Grad-CAM-based visualization of the model confirmed accurate attention to the most significant facial areas, resulting in better recognition of subtle expressions such as fear and contempt. The findings indicate that SE-Hybrid + Face-ViT is a computationally efficient yet highly discriminative FER strategy that successfully addresses the issue of how to preserve details while globally reasoning with contextual information locally. Full article

Portopulmonary hypertension (PoPH) and hepatopulmonary syndrome (HPS) present two vascular complications of portal hypertension, which make opposite extremes occur against the same pathophysiological background. In PoPH, vasoconstriction predominates, along with gradual remodeling of pulmonary arteries, while HPS develops due to pathological vasodilation and creation of intrapulmonary shunts. Even though they come about by different mechanisms, both disorders significantly affect quality of life, survival, and the possibility of liver transplant. In the early phases, in clinical practice, symptoms are mainly mild and nonspecific, and overlapping with symptoms of advanced liver disease often delays forming a diagnosis. In PoPH, elevated pressures in pulmonary arteries and increased vascular resistance are observed, while HPS exhibits arterial hypoxemia with normal or lowered pulmonary pressure. Standard diagnostic workup includes echocardiography, right-heart catheterization, and analysis of the arterial gases. In patients with severe PoPH, pronounced pulmonary hypertension can represent absolute contraindication for liver transplantation due to risk of acute right heart failure during operation. Conversely, HPS usually resolves itself after a successful transplant, which confirms that the transplant is an indication of being potentially curative. Therapeutic possibilities for both states are still limited. In PoPH, specific vasodilators and supportive measures are applied, while HPS treatment is mostly supportive, directed at maintaining oxygenation until the transplant. Future research should be focused on the development of targeted therapies that address vascular remodeling, angiogenesis, and oxidative stress, as well as on the standardization of diagnostic criteria and multicentric cooperation. This approach would facilitate earlier recognition, a precise assessment of transplantability, and a better long-term outcome for patients with portal hypertension and lung vascular complications. Key Points: Portopulmonary hypertension (PoPH) and hepatopulmonary syndrome (HPS) represent two opposite vascular complications of portal hypertension, posing distinct challenges for liver transplantation. This review summarizes their pathophysiology, diagnostic pathways, and therapeutic strategies, emphasizing the importance of hemodynamic profiling and multidisciplinary management to optimize transplant outcomes. Full article

In the scenario where the number of interceptors is equal to the number of target aircraft, and recognizing that the geometric configuration of interceptors during their maneuver towards targets affects detection effectiveness and guidance accuracy, we propose a Cooperative Detection and Guidance (CDG) method rooted in optimal control theory. This method optimizes detection by adjusting the line-of-sight (LOS) angle to minimize errors, and leverages the Fast Multiple Model Adaptive Estimation (Fast MMAE) algorithm to enhance interceptors’ ability to estimate the motion states and maneuver switching times of target aircraft, thereby boosting guidance accuracy. Results from 500 Monte Carlo simulations reveal that, compared to the Augmented Proportional Navigation (APN) guidance law, our integrated detection and guidance approach exhibits superior target recognition capabilities and achieves higher interception accuracy. Full article

Children with autism spectrum disorder (ASD) often display atypical emotional expressions and physiological responses, making emotion recognition challenging. This study proposes a multimodal recognition model employing a late fusion framework combining facial expression with physiological measures: electrodermal activity (EDA), temperature (TEMP), and heart rate (HR). Emotional states are annotated using two complementary schemes derived from a shared set of labels. Three annotators provide one categorical Ekman emotion for each timestamp. From these annotations, a majority-vote label identifies the dominant emotion, while a proportional distribution reflects the likelihood of each emotion based on the relative frequency of the annotators’ selections. Separate machine learning models are trained for each modality and for each annotation scheme, and their outputs are integrated through decision-level fusion. A distinct decision-level fusion model is constructed for each annotation scheme, ensuring that both the categorical and likelihood-based representations are optimally combined. The experiments on the EMBOA dataset, collected within the project “Affective loop in Socially Assistive Robotics as an intervention tool for children with autism”, show that the late fusion model achieves higher accuracy and robustness than unimodal baselines. The system attains an accuracy of 68% for categorical emotion classification and 78% under the likelihood-estimation scheme. The results obtained, although lower than those reported in other studies, suggest that further research into emotion recognition in autistic children using other fusions is warranted, even in the case of datasets with a significant number of missing values and low sample representation for certain emotions. Full article

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article

In the fight against growing energy poverty in Europe, remote and rural areas are most affected but play a crucial role in promoting a fair and sustainable transition. Furthermore, energy communities have been recognized as cost-efficient options and opportunities to enhance the active participation of citizens in electricity markets. Despite the wide recognition of their potential in alleviating energy poverty, evidence is still limited. This paper investigates the ‘missing links’ in producing clean energy through community-based practices in remote areas. This study presents a literature review aimed at identifying case studies at the European level to build a knowledge base on the state of the art in the context of the Green Deal. Of the 4422 publications found, we identified and analyzed 266 publications with one or more European cases. Of these, only 67 publications used keywords relevant to our research objective, which we further explored and categorized according to the primary purpose of the study, i.e., assessment, barriers and gaps, implementation, management and planning, modeling, and public opinion. Our results show that publications serve mainly to test a methodology for potential use and not to recount an experience, lacking practical application and policy integration. Nevertheless, we noticed a tendency to activate citizen engagement forms or gather perceptions to increase social acceptability. Full article

Introduction: In recent decades, the burden of mental disorders has become a major determinant of population health in the European Union, generating profound clinical, socioeconomic, and institutional consequences. Despite political recognition of this silent crisis, substantial methodological challenges persist in the transnational monitoring of mental health and in linking disease burden with the resources allocated to address it. The present analysis develops a multivariate taxonomy of EU Member States from a psychosocial perspective, using an integrative quantitative approach. Methods: This cross-sectional, comparative study follows international standards for transparent and reproducible quantitative reporting and is based on 18 harmonized clinical, epidemiological, and institutional indicators collected for 27 EU Member States over the period 2014–2023. The indicators used in this study were grouped according to their position along the care continuum. Hospital-based indicators refer to inpatient activity and institutional capacity, including total hospital discharges, psychiatric admissions (affective disorders, schizophrenia, dementia, alcohol- and drug-related disorders), and hospital bed availability. Outpatient and community-level indicators reflect the capacity of systems to provide non-hospital psychiatric care and consist primarily of psychiatrist density and total specialist medical workforce. Finally, subjective perception indicators capture population-level self-assessed health status, complementing clinical and institutional measures by integrating a psychosocial perspective. After harmonization and standardization, Principal Component Analysis (PCA) with Varimax rotation was applied to identify latent dimensions of mental health. Model adequacy was confirmed using the Kaiser–Meyer–Olkin coefficient (0.747) and Bartlett’s test of sphericity (p < 0.001). Results: Three latent dimensions explaining 77.7% of the total variance were identified: (1) institutionalized psychiatric burden, (2) functional capacity of the health care system, and (3) suicidal vulnerability associated with problematic substance use. Standardized factor scores allowed for the classification of Member States, revealing distinct patterns of psychosocial risk. For example, Germany and France display profiles marked by high levels of institutionalized psychiatric activity, while the Baltic and Southeast European countries exhibit elevated suicidal vulnerability in the context of limited medical resources. These results highlight the deep heterogeneity of psychiatric configurations in Europe and reveal persistent gaps between population needs and institutional response capacity. Conclusions: The analysis provides an empirical foundation for differentiated public policies aimed at prevention, early intervention, and stigma reduction. It also supports the case for institutionalizing a European mental health monitoring system based on harmonized indicators and common assessment standards. Overall, the findings clarify the underlying structure of mental health across the European Union and underscore the need for coherent, evidence-based strategies to reduce inequalities and strengthen system performance at the continental level. Full article

Human activity recognition (HAR) based on wearable sensors is a key technology in the fields of smart sensing and health monitoring. With the rapid development of deep learning, its powerful feature extraction capabilities have significantly enhanced recognition performance and reduced reliance on traditional handcrafted feature engineering. However, current deep learning models still face challenges in effectively capturing complex temporal dependencies in long-term time-series sensor data and addressing information redundancy, which affect model recognition accuracy and generalization ability. To address these issues, this paper proposes an innovative CNN-BiGRU–Transformer hybrid deep learning model aimed at improving the accuracy and robustness of human activity recognition. The proposed model integrates a multi-branch Convolutional Neural Network (CNN) to effectively extract multi-scale local spatial features, and combines a Bidirectional Gated Recurrent Unit (BiGRU) with a Transformer hybrid module for modeling temporal dependencies and extracting temporal features in long-term time-series data. Additionally, an attention mechanism is incorporated to dynamically allocate weights, suppress redundant information, and enhance key features, further improving recognition performance. To demonstrate the capability of the proposed model, evaluations are performed on three public datasets: WISDM, PAMAP2, and UCI-HAR. The model achieved recognition accuracies of 98.41%, 95.62%, and 96.74% on the three datasets, respectively, outperforming several state-of-the-art methods. These results confirm that the proposed approach effectively addresses feature extraction and redundancy challenges in long-term sensor time-series data and provides a robust solution for wearable sensor-based human activity recognition. Full article

Background/Objectives: Complex emotions in daily life often arise as mixtures of basic emotions, but most emotion-recognition systems still target a small set of discrete states and rely on contact-based sensing. This study aimed (1) to examine whether four compound emotions—Positive Surprise, Negative Surprise, Positive Sadness, and Negative Sadness—defined by valence direction within basic emotion categories can be differentiated using heart rate variability (HRV), and (2) to evaluate the feasibility of a camera-based contactless system (Deep Health Vision System, DHVS) by comparing it with a reference chest-strap device (Polar H10). Methods: Ten healthy adults viewed video clips designed to induce the four complex emotions. HRV was recorded simultaneously using Polar H10 and a webcam-based rPPG implementation of DHVS. Two-minute baseline and during-stimulus segments were extracted, and change rates of standard HRV indices were computed. After each stimulus, participants reported Valence, Arousal, Dominance, and proportional basic-emotion composition. Statistical analyses examined within-condition HRV changes, associations between HRV and self-reports, differences across emotion/valence conditions, and concordance between DHVS and Polar H10. Results: Self-reports confirmed distinct affective profiles for the four compound emotions. Positive and Negative Surprise were associated with heart rate reduction, while Positive Sadness showed reduced total power; Negative Sadness yielded heterogeneous but nonsignificant HRV changes. Specific HRV indices demonstrated condition-dependent correlations with Valence, Arousal, and Dominance. LF/HF changes were more sensitive to emotion category (Surprise vs. Sadness), whereas total power changes were more sensitive to valence (positive vs. negative). DHVS partially reproduced Polar H10 HRV patterns, with clearer concordance under positive-valence conditions. Conclusions: HRV captures distinct autonomic signatures of complex emotions defined by valence direction and shows meaningful links with subjective affective evaluations. LF/HF and total power provide complementary information on emotion category and valence-related autonomic reactivity, supporting indicator-specific modeling strategies. DHVS shows preliminary feasibility as a contactless HRV sensing platform for complex emotion recognition, warranting further validation with larger samples and more robust rPPG processing. Full article

In healthy cells, the cytomatrix mechanics utilize mitochondrial respiration to control cytosolic motion and fine-tune the chemical processes. In cancer, the cytosolic motion is energized by glycolytic fermentation (the Warburg effect), which provides additional energy to supply the needs of the cytomatrix. Here, we describe the physical and chemical processes of the integrated and cooperative cytomatrix cytoarchitecture, in which structure and function are inseparable. The extracellular matrix is interconnected with the intracellular cytomatrix and functions as two integrated elastic solid phases. This finding led us to propose mechanisms of tumor microenvironment formation resulting from the mutational burden, in which altered proteins with corresponding post-translational modifications translocate to the cell surface, where they attract immunocompetent cells and activated fibroblasts, producing a tumor-insulating niche. This insulation disrupts cell-to-cell recognition and other signaling pathways that affect the intracellular cytomatrix, particularly actin dynamics, which influence both cell size and shape, recognized as the dedifferentiated state of cancer cells. We also discuss the perspectives of AI in cytomatrix modeling and neural network modeling, focusing on the effects of intracellular and extracellular matrices on the development of the tumor microenvironment. Full article

EEG remains the primary diagnostic tool for evaluating seizures in children, with interictal epileptiform discharges (IEDs) serving as key biomarkers of epileptogenic activity. However, not all IEDs have the same prognostic significance. Variations in IED topography, morphology, frequency, and timing influence outcomes in pediatric epilepsy. The developing brain’s maturation affects IED location and features, creating age-specific patterns with distinct implications. For example, occipital and midline IEDs are common in young children, with midline IEDs strongly linked to increased seizures and developmental delay than control patients. Morphological features provide additional prognostic stratification. While centrotemporal IEDs with tangential dipoles are well-established as favorable prognostic markers, IEDs exhibiting tangential dipoles in any brain region are associated with more benign clinical courses than control patients. Conversely, positive sharp waves persisting beyond the neonatal period signal less favorable prognosis, including developmental delay, abnormal neurological examination, and structural brain abnormalities. Additionally, IEDs occurring on ripples have been shown to serve as more reliable interictal biomarkers of the epileptogenic zone than IEDs or ripples alone. Topography, frequency and sleep-state dependence also carry clinical significance, as frequent IEDs during slow-wave sleep may impact cognition. Furthermore, the temporal context of IED occurrence during seizure onset, treatment, activation procedures, medication withdrawal, or after epilepsy surgery provides valuable prognostic information. Recognition of these nuanced electrophysiological distinctions enhances clinicians’ ability to predict clinical trajectories and optimize long-term management strategies. Full article