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Search Results (1,348)

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38 pages, 1412 KB  
Article
A Framework for Understanding the Impact of Integrating Conceptual and Quantitative Reasoning in a Quantum Optics Tutorial on Students’ Conceptual Understanding
by Paul D. Justice, Emily Marshman and Chandralekha Singh
Educ. Sci. 2025, 15(10), 1314; https://doi.org/10.3390/educsci15101314 - 3 Oct 2025
Abstract
We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of [...] Read more.
We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article
13 pages, 261 KB  
Article
Cultural Hybridity and Parenting Styles: Analyzing Authoritative and Authoritarian Dynamics in Hong Kong
by Annis Lai Chu Fung and Yuqi Deng
Soc. Sci. 2025, 14(10), 584; https://doi.org/10.3390/socsci14100584 - 1 Oct 2025
Abstract
In Hong Kong, the interaction between traditional values and modern influences creates a unique cultural landscape that influences family dynamics, intergenerational communication, and adolescent mental health. This study aimed to fill critical research gaps by exploring the relationship between authoritative and authoritarian parenting [...] Read more.
In Hong Kong, the interaction between traditional values and modern influences creates a unique cultural landscape that influences family dynamics, intergenerational communication, and adolescent mental health. This study aimed to fill critical research gaps by exploring the relationship between authoritative and authoritarian parenting styles within this hybrid cultural context. Parenting style scores were based on the PSDQ-26 questionnaires completed by both parents of 2325 students. These students also provided demographic data used in the analysis (1013 girls, Mage = 13.35, SD = 1.22). The data analysis examined the correlations between parenting styles and variations across gender and age groups. Contrary to patterns observed in Western contexts, the results indicated no significant correlation between authoritative and authoritarian parenting styles (r = 0.02, p > 0.05), suggesting a complex coexistence influenced by Hong Kong’s hybrid sociocultural context. Notably, the study revealed gender-based differences: boys’ parents reported higher levels of democratic participation and reasoning, reflecting authoritative parenting, while also showing greater use of physical coercion and punitive discipline, indicative of authoritarian parenting. Authoritative parenting, but not authoritarian parenting, showed a decline as children matured. By investigating these dynamics, the study not only addresses a significant gap in the literature but also enhances the understanding of how cultural and developmental factors shape parenting practices. These insights are crucial for developing culturally adapted parenting education materials and informing interventions that support child development in diverse cultural settings. Full article
(This article belongs to the Section Family Studies)
25 pages, 6901 KB  
Article
Improving Active Support Capability: Optimization and Scheduling of Village-Level Microgrid with Hybrid Energy Storage System Containing Supercapacitors
by Yu-Rong Hu, Jian-Wei Ma, Ling Miao, Jian Zhao, Xiao-Zhao Wei and Jing-Yuan Yin
Eng 2025, 6(10), 253; https://doi.org/10.3390/eng6100253 - 1 Oct 2025
Abstract
With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in [...] Read more.
With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article
(This article belongs to the Section Electrical and Electronic Engineering)
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46 pages, 6024 KB  
Review
Recent Advances in Transition Metal Selenide-Based Catalysts for Organic Pollutant Degradation by Advanced Oxidation Processes
by Donatos Manos and Ioannis Konstantinou
Catalysts 2025, 15(10), 938; https://doi.org/10.3390/catal15100938 - 1 Oct 2025
Abstract
In recent years, one of the major problems facing humanity has been the contamination of the environment by various organic pollutants, with some of them exhibiting environmental persistence or pseudo-persistence. For this reason, it is necessary today, more than ever, to find new [...] Read more.
In recent years, one of the major problems facing humanity has been the contamination of the environment by various organic pollutants, with some of them exhibiting environmental persistence or pseudo-persistence. For this reason, it is necessary today, more than ever, to find new and effective methods for degrading these persistent pollutants. Transition metal selenides (TMSes) have emerged as a versatile and promising class of catalysts for the degradation of organic pollutants through various advanced oxidation processes (AOPs). The widespread use of these materials lies in the desirable characteristics they offer, such as unique electronic structures, narrow band gaps, high electrical conductivity, and multi-valent redox behavior. This review comprehensively examines recent progress in the design, synthesis, and application of these TMSes—including both single- and composite systems, such as TMSes/g-C3N4, TMSes/TiO2, and heterojunctions. The catalytic performance of these systems is being highlighted, regarding the degradation of organic pollutants such as dyes, pharmaceuticals, antibiotics, personal care products, etc. Further analysis of the mechanistic insights, structure–activity relationships, and operational parameter effects are critically discussed. Emerging trends, such as hybrid AOPs combining photocatalysis with PMS or electro-activation, and the challenges of stability, scalability, and real wastewater applicability are explored in depth. Finally, future directions emphasize the integration of multifunctional activation methods for the degradation of organic pollutants. This review aims to provide a comprehensive analysis and pave the way for the utilization of TMSe catalysts in sustainable and efficient wastewater remediation technologies. Full article
(This article belongs to the Collection Catalysis in Advanced Oxidation Processes for Pollution Control)
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22 pages, 1797 KB  
Article
A Novel Hybrid Deep Learning–Probabilistic Framework for Real-Time Crash Detection from Monocular Traffic Video
by Reşat Buğra Erkartal and Atınç Yılmaz
Appl. Sci. 2025, 15(19), 10523; https://doi.org/10.3390/app151910523 - 29 Sep 2025
Abstract
The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman [...] Read more.
The rapid evolution of autonomous vehicle technologies has amplified the need for crash detection that operates robustly under complex traffic conditions with minimal latency. We propose a hybrid temporal hierarchy that augments a Region-based Convolutional Neural Network (R-CNN) with an adaptive time-variant Kalman filter (with total-variation prior), a Hidden Markov Model (HMM) for state stabilization, and a lightweight Artificial Neural Network (ANN) for learned temporal refinement, enabling real-time crash detection from monocular video. Evaluated on simulated traffic in CARLA and real-world driving in Istanbul, the full temporal stack achieves the best precision–recall balance, yielding 83.47% F1 offline and 82.57% in real time (corresponding to 94.5% and 91.2% detection accuracy, respectively). Ablations are consistent and interpretable: removing the HMM reduces F1 by 1.85–2.16 percentage points (pp), whereas removing the ANN has a larger impact of 2.94–4.58 pp, indicating that the ANN provides the largest marginal gains—especially under real-time constraints. The transition from offline to real time incurs a modest overall loss (−0.90 pp F1), driven more by recall than precision. Compared to strong single-frame baselines, YOLOv10 attains 82.16% F1 and a real-time Transformer detector reaches 82.41% F1, while our full temporal stack remains slightly ahead in real time and offers a more favorable precision–recall trade-off. Notably, integrating the ANN into the HMM-based pipeline improves accuracy by 2.2%, while the time-variant Kalman configuration reduces detection lag by approximately 0.5 s—an improvement that directly addresses the human reaction time gap. Under identical conditions, the best RCNN-based configuration yields AP@0.50 ≈ 0.79 with an end-to-end latency of 119 ± 21 ms per frame (~8–9 FPS). Overall, coupling deep learning with probabilistic reasoning yields additive temporal benefits and advances deployable, camera-only crash detection that is cost-efficient and scalable for intelligent transportation systems. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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32 pages, 2754 KB  
Article
Critical Thinking Writing Assessment in Middle School Language: Logic Chain Extraction and Expert Score Correlation Test Using BERT-CNN Hybrid Model
by Yao Wu and Qin-Hua Zheng
Appl. Sci. 2025, 15(19), 10504; https://doi.org/10.3390/app151910504 - 28 Sep 2025
Abstract
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 [...] Read more.
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
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37 pages, 16383 KB  
Article
Generating Realistic Urban Patterns: A Controllable cGAN Approach with Hybrid Loss Optimization
by Amgad Agoub and Martin Kada
ISPRS Int. J. Geo-Inf. 2025, 14(10), 375; https://doi.org/10.3390/ijgi14100375 - 25 Sep 2025
Abstract
This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a [...] Read more.
This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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30 pages, 4822 KB  
Article
Combining Deep Learning Architectures with Fuzzy Logic for Robust Pneumonia Detection in Chest X-Rays
by Azeddine Mjahad and Alfredo Rosado-Muñoz
Appl. Sci. 2025, 15(19), 10321; https://doi.org/10.3390/app151910321 - 23 Sep 2025
Viewed by 186
Abstract
Early and accurate detection of pneumonia from chest X-ray images is essential for improving treatment and clinical outcomes. Medical imaging datasets often exhibit class imbalance and uncertainty in feature extraction, which complicates conventional classification methods and motivates the use of advanced approaches combining [...] Read more.
Early and accurate detection of pneumonia from chest X-ray images is essential for improving treatment and clinical outcomes. Medical imaging datasets often exhibit class imbalance and uncertainty in feature extraction, which complicates conventional classification methods and motivates the use of advanced approaches combining deep learning and fuzzy logic. This study proposes a hybrid approach that combines deep learning architectures (VGG16, EfficientNetV2, MobileNetV2, ResNet50) for feature extraction with fuzzy logic-based classifiers, including Fuzzy C-Means, Fuzzy Decision Tree, Fuzzy KNN, Fuzzy SVM, and ANFIS (Adaptive Neuro-Fuzzy Inference System). Feature selection techniques were also applied to enhance the discriminative power of the extracted features. The best-performing model, ANFIS with MobileNetV2 features and Gaussian membership functions, achieved an overall accuracy of 98.52%, with Normal class precision of 97.07%, recall of 97.48%, and F1-score of 97.27%, and Pneumonia class precision of 99.06%, recall of 98.91%, and F1-score of 98.99%. Among the fuzzy classifiers, Fuzzy SVM and Fuzzy KNN also showed strong performance with accuracy above 96%, while Fuzzy Decision Tree and Fuzzy C-Means achieved moderate results. These findings demonstrate that integrating deep feature extraction with neuro-fuzzy reasoning significantly improves diagnostic accuracy and robustness, providing a reliable tool for clinical decision support. Future research will focus on optimizing model efficiency, interpretability, and real-time applicability. Full article
(This article belongs to the Special Issue Machine Learning-Based Feature Extraction and Selection: 2nd Edition)
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30 pages, 12687 KB  
Article
Q-MobiGraphNet: Quantum-Inspired Multimodal IoT and UAV Data Fusion for Coastal Vulnerability and Solar Farm Resilience
by Mohammad Aldossary
Mathematics 2025, 13(18), 3051; https://doi.org/10.3390/math13183051 - 22 Sep 2025
Viewed by 223
Abstract
Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to [...] Read more.
Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article
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22 pages, 3553 KB  
Article
An Extended Epistemic Framework Beyond Probability for Quantum Information Processing with Applications in Security, Artificial Intelligence, and Financial Computing
by Gerardo Iovane
Entropy 2025, 27(9), 977; https://doi.org/10.3390/e27090977 - 18 Sep 2025
Viewed by 199
Abstract
In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum [...] Read more.
In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms. Full article
(This article belongs to the Special Issue Probability Theory and Quantum Information)
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25 pages, 5313 KB  
Article
An Interpretable Hybrid Fault Prediction Framework Using XGBoost and a Probabilistic Graphical Model for Predictive Maintenance: A Case Study in Textile Manufacturing
by Fernando Velasco-Loera, Mildreth Alcaraz-Mejia and Jose L. Chavez-Hurtado
Appl. Sci. 2025, 15(18), 10164; https://doi.org/10.3390/app151810164 - 18 Sep 2025
Cited by 1 | Viewed by 358
Abstract
This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between [...] Read more.
This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article
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16 pages, 220 KB  
Article
Cognitive Integration for Hybrid Collective Agency
by Ruili Wang
Philosophies 2025, 10(5), 103; https://doi.org/10.3390/philosophies10050103 - 16 Sep 2025
Viewed by 280
Abstract
Can human–machine hybrid systems (HMHs) constitute genuine collective agents? This paper defends an affirmative answer. I argue that HMHs achieve collective intentionality without shared consciousness by satisfying the following three functional criteria: goal alignment, functional complementarity, and stable interactivity. Against this functionalist account, [...] Read more.
Can human–machine hybrid systems (HMHs) constitute genuine collective agents? This paper defends an affirmative answer. I argue that HMHs achieve collective intentionality without shared consciousness by satisfying the following three functional criteria: goal alignment, functional complementarity, and stable interactivity. Against this functionalist account, the following two objections arise: (1) the cognitive bloat problem, that functional criteria cannot distinguish genuine cognitive integration from mere tool use; and (2) the phenomenological challenge, that AI’s lack of practical reason reduces human–AI interaction to subject–tool relations. I respond by distinguishing constitutive from instrumental functional contributions and showing that collective agency requires stable functional integration, not phenomenological fusion. The result is what I call Functional Hybrid Collective Agents (FHCAs), which are systems exhibiting irreducible collective intentionality through deep human–AI coupling. Full article
(This article belongs to the Special Issue Collective Agency and Intentionality)
36 pages, 1495 KB  
Review
Decision-Making for Path Planning of Mobile Robots Under Uncertainty: A Review of Belief-Space Planning Simplifications
by Vineetha Malathi, Pramod Sreedharan, Rthuraj P R, Vyshnavi Anil Kumar, Anil Lal Sadasivan, Ganesha Udupa, Liam Pastorelli and Andrea Troppina
Robotics 2025, 14(9), 127; https://doi.org/10.3390/robotics14090127 - 15 Sep 2025
Viewed by 958
Abstract
Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and [...] Read more.
Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy. Full article
(This article belongs to the Section Sensors and Control in Robotics)
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31 pages, 3576 KB  
Article
UltraScanNet: A Mamba-Inspired Hybrid Backbone for Breast Ultrasound Classification
by Alexandra-Gabriela Laicu-Hausberger and Călin-Adrian Popa
Electronics 2025, 14(18), 3633; https://doi.org/10.3390/electronics14183633 - 13 Sep 2025
Viewed by 317
Abstract
Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification [...] Read more.
Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification needs. The proposed architecture combines a convolutional stem with learnable 2D positional embeddings, followed by a hybrid stage that unites MobileViT blocks with spatial gating and convolutional residuals and two progressively global stages that use a depth-aware composition of three components: (1) UltraScanUnit (a state-space module with selective scan gated convolutional residuals and low-rank projections), (2) ConvAttnMixers for spatial channel mixing, and (3) multi-head self-attention blocks for global reasoning. This research includes a detailed ablation study to evaluate the individual impact of each architectural component. The results demonstrate that UltraScanNet reaches 91.67% top-1 accuracy, a precision score of 0.9072, a recall score of 0.9174, and an F1-score of 0.9096 on the BUSI dataset, which make it a very competitive option among multiple state-of-the-art models, including ViT-Small (91.67%), MaxViT-Tiny (91.67%), MambaVision (91.02%), Swin-Tiny (90.38%), ConvNeXt-Tiny (89.74%), and ResNet-50 (85.90%). On top of this, the paper provides an extensive global and per-class analysis of the performance of these models, offering a comprehensive benchmark for future work. The code will be publicly available. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data Processing in Healthcare)
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22 pages, 605 KB  
Perspective
Delivering Musculoskeletal Rehabilitation in the Digital Era: A Perspective on Clinical Strategies for Remote Practice
by Muhammad Alrwaily
Healthcare 2025, 13(18), 2286; https://doi.org/10.3390/healthcare13182286 - 12 Sep 2025
Viewed by 695
Abstract
The purpose of this perspective is to present a structured framework for delivering musculoskeletal (MSK) care via telerehabilitation, advocating for a fundamental shift in the mindset of physical therapists. Rather than viewing virtual care as a limited substitute, it is redefined as a [...] Read more.
The purpose of this perspective is to present a structured framework for delivering musculoskeletal (MSK) care via telerehabilitation, advocating for a fundamental shift in the mindset of physical therapists. Rather than viewing virtual care as a limited substitute, it is redefined as a clinically valid model that requires deliberate reengineering of traditional assessment and treatment practices. The article addresses three key questions: (1) How can MSK assessment and treatment be effectively delivered in the digital environment? (2) What clinical reasoning pathways can guide patient triage in virtual care? and (3) What value does telerehabilitation offer to both patients and therapists? The article outlines how MSK sessions can be conducted remotely through a systematic approach to preparation, subjective examination, and physical assessment, each adapted to both the constraints and opportunities of the digital environment. Core elements of in-person care are translated into telehealth-compatible formats, including visual observation, patient-guided special tests, and digitally administered patient-reported outcome measures. It further proposes clinical decision pathways that enable therapists to triage patients into three categories: those fully suitable for telehealth, those requiring hybrid care, and those needing referral. The value proposition of MSK telerehabilitation is discussed from both the patient and therapist perspectives, highlighting enhanced accessibility, efficiency, and patient empowerment. The article contrasts the in-person and telerehabilitation models, underscoring the elevated importance of communication, creativity, resourcefulness, and clinical reasoning in virtual contexts. Beyond current challenges such as regulatory ambiguity, reimbursement variability, and digital inequity, the article explores future directions for MSK care. These include integration of wearable technologies, AI-assisted assessments, and an evolving therapist role as a director of care within a digitally enabled system. Ultimately, this article offers not just a model for virtual MSK sessions, but a vision for sustainable, evidence-informed transformation in rehabilitation delivery. Full article
(This article belongs to the Section Digital Health Technologies)
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