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25 pages, 2474 KB  
Article
Data Augmentation-Enhanced Myocardial Infarction Classification and Localization Using a ResNet-Transformer Cascaded Network
by Yunfan Chen, Qi Gao, Jinxing Ye, Yuting Li and Xiangkui Wan
Biology 2025, 14(10), 1425; https://doi.org/10.3390/biology14101425 - 16 Oct 2025
Abstract
Accurate diagnosis of myocardial infarction (MI) holds significant clinical importance for public health systems. Deep learning-based ECG, classification and localization methods can automatically extract features, thereby overcoming the dependence on manual feature extraction in traditional methods. However, these methods still face challenges such [...] Read more.
Accurate diagnosis of myocardial infarction (MI) holds significant clinical importance for public health systems. Deep learning-based ECG, classification and localization methods can automatically extract features, thereby overcoming the dependence on manual feature extraction in traditional methods. However, these methods still face challenges such as insufficient utilization of dynamic information in cardiac cycles, inadequate ability to capture both global and local features, and data imbalance. To address these issues, this paper proposes a ResNet-Transformer cascaded network (RTCN) to process time frequency features of ECG signals generated by the S-transform. First, the S-transform is applied to adaptively extract global time frequency features from the time frequency domain of ECG signals. Its scalable Gaussian window and high phase resolution can effectively capture the dynamic changes in cardiac cycles that traditional methods often fail to extract. Then, we develop an architecture that combines the Transformer attention mechanism with ResNet to extract multi-scale local features and global temporal dependencies collaboratively. This compensates for the existing deep learning models’ insufficient ability to capture both global and local features simultaneously. To address the data imbalance problem, the Denoising Diffusion Probabilistic Model (DDPM) is applied to synthesize high-quality ECG samples for minority classes, increasing the inter-patient accuracy from 61.66% to 68.39%. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization confirms that the model’s attention areas are highly consistent with pathological features, verifying its clinical interpretability. Full article
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13 pages, 1418 KB  
Article
Investigating the “Dark” Genome: First Report of Partington Syndrome in Cyprus
by Constantia Aristidou, Athina Theodosiou, Pavlos Antoniou, Angelos Alexandrou, Ioannis Papaevripidou, Ludmila Kousoulidou, Pantelitsa Koutsou, Anthi Georghiou, Türem Delikurt, Elena Spanou, Nicole Salameh, Paola Evangelidou, Kyproula Christodoulou, Alain Verloes, Violetta Christophidou-Anastasiadou, George A. Tanteles and Carolina Sismani
Genes 2025, 16(10), 1224; https://doi.org/10.3390/genes16101224 - 15 Oct 2025
Abstract
Background/Objectives: X-linked intellectual disability (XLID) is a highly heterogeneous disorder accounting for ~10% of all males with ID. Next-generation sequencing (NGS) has revolutionized the discovery of causal XLID genes and variants; however, many cases remain unresolved. We present a four-generation syndromic XLID [...] Read more.
Background/Objectives: X-linked intellectual disability (XLID) is a highly heterogeneous disorder accounting for ~10% of all males with ID. Next-generation sequencing (NGS) has revolutionized the discovery of causal XLID genes and variants; however, many cases remain unresolved. We present a four-generation syndromic XLID family with multiple males exhibiting variable degree of ID, focal dystonia and epilepsy. Methods: Extensive cytogenetic and targeted genetic testing was initially performed, followed by whole-exome sequencing (WES) and short-read whole-genome sequencing (WGS). Apart from the routine NGS analysis pipelines, sequencing data was revisited by focusing on poorly covered/mapped regions on chromosome X (chrX), to potentially reveal unidentified clinically relevant variants. Candidate variant validation and family segregation analysis were performed with Sanger sequencing. Results: All initial diagnostic testing was negative. Subsequently, 300 previously reported “dark” chrX coding DNA sequences, overlapping 97 genes, were cross-checked against 29 chrX genes highly associated (p < 0.05) with ID and focal dystonia, according to Phenomizer. Manual inspection of the existing NGS data in two low-coverage regions, chrX:25013469-25013696 and chrX:111744737-111744820 (hg38), revealed a recurrent pathogenic ARX variant NM_139058.3:c.441_464dup p.(Ala148_Ala155dup) (ARXdup24) associated with non-syndromic or syndromic XLID, including Partington syndrome. Sanger sequencing confirmed ARXdup24 in all affected males, with carrier status in their unaffected mothers, and absence in other unaffected relatives. Conclusions: After several years of diagnostic odyssey, the pathogenic ARXdup24 variant was unmasked, supporting a genotype–phenotype correlation in the first Partington syndrome family in Cyprus. This study highlights that re-examining underrepresented genomic regions and using phenotype-driven tools can provide critical diagnostic insights in unresolved XLID cases. Full article
(This article belongs to the Special Issue Molecular Basis and Genetics of Intellectual Disability)
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18 pages, 319 KB  
Perspective
Mental Health of Young People in the Post-Pandemic Era: Perspective Based on Positive Psychology and Resilience
by Daniel T. L. Shek
Int. J. Environ. Res. Public Health 2025, 22(10), 1574; https://doi.org/10.3390/ijerph22101574 - 15 Oct 2025
Abstract
With the gradual decline in COVID-19 cases, there is a need to re-visit the mental health of adolescents and emerging adults in the post-pandemic period. Several observations can be highlighted from the scientific literature. First, while some studies suggest that mental health of [...] Read more.
With the gradual decline in COVID-19 cases, there is a need to re-visit the mental health of adolescents and emerging adults in the post-pandemic period. Several observations can be highlighted from the scientific literature. First, while some studies suggest that mental health of young people has worsened in the post-pandemic period, there are inconsistent and conflicting findings. Second, there are more studies on psychological morbidity than on positive psychological attributes. Third, compared with the West, there are relatively fewer Chinese studies. Fourth, compared with adolescents, there are relatively fewer studies on emerging adults. Based on these observations of the existing literature, I have detailed several reflections on the mental health of young people, including enhancing positive psychological attributes in young people through positive youth development (PYD) programs, building up the individual resilience of young people, strengthening family resilience, adopting multidisciplinary, interdisciplinary and transdisciplinary approaches in understanding the mental health of young people, building more well-articulated theoretical models, charting future research directions, and developing intervention strategies in the post-pandemic period. Full article
(This article belongs to the Special Issue Perspectives in Behavioral and Mental Health)
28 pages, 31477 KB  
Article
Contextual Feature Fusion-Based Keyframe Selection Using Semantic Attention and Diversity-Aware Optimization for Video Summarization
by Chitrakala S and Aparyay Kumar
Symmetry 2025, 17(10), 1737; https://doi.org/10.3390/sym17101737 - 15 Oct 2025
Abstract
Training-free video summarization tackles the challenge of selecting the most informative keyframes from a video without relying on costly training or complex deep models. This work introduces C2FVS-DPP (Contextual Feature Fusion Video Summarization with Determinantal Point Process), a lightweight framework that [...] Read more.
Training-free video summarization tackles the challenge of selecting the most informative keyframes from a video without relying on costly training or complex deep models. This work introduces C2FVS-DPP (Contextual Feature Fusion Video Summarization with Determinantal Point Process), a lightweight framework that generates concise video summaries by jointly modeling semantic importance, visual diversity, temporal structure, and {symmetry. The design centers on a symmetry-aware fusion strategy, where appearance, motion, and semantic cues are aligned in a unified embedding space, and on a reward-guided optimization logic that balances representativeness and diversity. Specifically, appearance features from ResNet-50, motion cues from optical flow, and semantic representations from BERT-encoded BLIP captions are fused into a contextual embedding. A Transformer encoder assigns importance scores, followed by shot boundary detection and K-Medoids clustering to identify candidate keyframes. These candidates are refined through a reward-based re-ranking mechanism that integrates semantic relevance, representativeness, and visual uniqueness, while a Determinantal Point Process (DPP) enforces globally diverse selection under a keyframe budget. To enable reliable evaluation, enhanced versions of the SumMe and TVSum50 datasets were curated to reduce redundancy and increase semantic density. On these curated benchmarks, C2FVS-DPP achieves F1-scores of 0.22 and 0.43 and fidelity scores of 0.16 and 0.40 on SumMe and TVSum50, respectively, surpassing existing models on both metrics. In terms of compression ratio, the framework records 0.9959 on SumMe and 0.9940 on TVSum50, remaining highly competitive with the best-reported values of 0.9981 and 0.9983. These results highlight the strength of C2FVS-DPP as an inference-driven, symmetry-aware, and resource-efficient solution for video summarization. Full article
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19 pages, 4569 KB  
Article
NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis
by Saeka Rahman, Md Motiur Rahman, Smriti Bhatt, Raji Sundararajan and Miad Faezipour
Bioengineering 2025, 12(10), 1107; https://doi.org/10.3390/bioengineering12101107 - 15 Oct 2025
Abstract
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. [...] Read more.
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance. Full article
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15 pages, 262 KB  
Review
How Close Are We to Achieving Durable and Efficacious Gene Therapy for Hemophilia A and B?
by Patrycja Sosnowska-Sienkiewicz and Danuta Januszkiewicz-Lewandowska
Genes 2025, 16(10), 1200; https://doi.org/10.3390/genes16101200 - 14 Oct 2025
Viewed by 10
Abstract
Hemophilia, an X-linked recessive bleeding disorder, results from mutations in the F8 or F9 genes, leading to factor VIII (hemophilia A) or factor IX (hemophilia B) deficiency. While conventional treatment relies on regular factor replacement therapy, gene therapy has emerged as a promising [...] Read more.
Hemophilia, an X-linked recessive bleeding disorder, results from mutations in the F8 or F9 genes, leading to factor VIII (hemophilia A) or factor IX (hemophilia B) deficiency. While conventional treatment relies on regular factor replacement therapy, gene therapy has emerged as a promising alternative, offering the potential for sustained endogenous factor production after a single administration. This review provides an in-depth analysis of recent advances in gene therapy for both hemophilia A and B, with a focus on AAV-mediated liver-directed approaches and other approved modalities. Key limitations—such as vector immunogenicity, hepatic toxicity, waning transgene expression, and limited re-dosing capacity—are discussed. Additional gene delivery platforms, including lentiviral and retroviral vectors, genome editing techniques (e.g., CRISPR/Cas9), and non-viral systems like transposons and lipid nanoparticles, are also examined. Although gene therapy for hemophilia B demonstrates greater clinical durability, hemophilia A presents unique challenges due to factor VIII’s size, poor expression efficiency, and the need for higher vector doses. Future efforts will focus on overcoming immune barriers, improving delivery technologies, and developing approaches suitable for pediatric patients and individuals with pre-existing immunity. This review provides not only a descriptive overview but also a critical comparison of gene therapy approaches for hemophilia A and B. We emphasize that the durability of response is currently superior in hemophilia B, whereas hemophilia A still faces unique barriers, including declining FVIII expression and higher immunogenicity. By analyzing cross-platform challenges (AAV, lentiviral, CRISPR, and emerging LNPs), we highlight the most promising strategies for overcoming these limitations and provide a forward-looking perspective on the future of gene therapy. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
21 pages, 3081 KB  
Article
Lightweight CNN–Transformer Hybrid Network with Contrastive Learning for Few-Shot Noxious Weed Recognition
by Ruiheng Li, Boda Yu, Boming Zhang, Hongtao Ma, Yihan Qin, Xinyang Lv and Shuo Yan
Horticulturae 2025, 11(10), 1236; https://doi.org/10.3390/horticulturae11101236 - 13 Oct 2025
Viewed by 109
Abstract
In resource-constrained edge agricultural environments, the accurate recognition of toxic weeds poses dual challenges related to model lightweight design and the few-shot generalization capability. To address these challenges, a multi-strategy recognition framework is proposed, which integrates a lightweight backbone network, a pseudo-labeling guidance [...] Read more.
In resource-constrained edge agricultural environments, the accurate recognition of toxic weeds poses dual challenges related to model lightweight design and the few-shot generalization capability. To address these challenges, a multi-strategy recognition framework is proposed, which integrates a lightweight backbone network, a pseudo-labeling guidance mechanism, and a contrastive boundary enhancement module. This approach is designed to improve deployment efficiency on low-power devices while ensuring high accuracy in identifying rare toxic weed categories. The proposed model achieves a real-time inference speed of 18.9 FPS on the Jetson Nano platform, with a compact model size of 18.6 MB and power consumption maintained below 5.1 W, demonstrating its efficiency for edge deployment. In standard classification tasks, the model attains 89.64%, 87.91%, 88.76%, and 88.43% in terms of precision, recall, F1-score, and accuracy, respectively, outperforming existing mainstream lightweight models such as ResNet18, MobileNetV2, and MobileViT across all evaluation metrics. In few-shot classification tasks targeting rare toxic weed species, the complete model achieves an accuracy of 80.32%, marking an average improvement of over 13 percentage points compared to ablation variants that exclude pseudo-labeling and self-supervised modules or adopt a CNN-only architecture. The experimental results indicate that the proposed model not only delivers strong overall classification performance but also exhibits superior adaptability for deployment and robustness in low-data regimes, offering an effective solution for the precise identification and ecological control of toxic weeds within intelligent agricultural perception systems. Full article
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28 pages, 65254 KB  
Article
SAM-Based Few-Shot Learning for Coastal Vegetation Segmentation in UAV Imagery via Cross-Matching and Self-Matching
by Yunfan Wei, Zhiyou Guo, Conghui Li, Weiran Li and Shengke Wang
Remote Sens. 2025, 17(20), 3404; https://doi.org/10.3390/rs17203404 - 10 Oct 2025
Viewed by 326
Abstract
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations [...] Read more.
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-2i, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article
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14 pages, 2482 KB  
Article
Computed Tomography Volumetric Measurements of Adrenal Glands in 26 Dogs Under One Year of Age: A Retrospective Study
by Julia Topmöller, Johanna Rieder, Sebastian Meller, Kerstin von Pückler, Holger Volk and Kristina Merhof
Vet. Sci. 2025, 12(10), 974; https://doi.org/10.3390/vetsci12100974 - 10 Oct 2025
Viewed by 197
Abstract
Limited data exist regarding the size and volume of adrenal glands in puppies; therefore, the present research aims to describe volumetric and traditional measurements of adrenal glands in computed tomography (CT) images of 26 dogs under 1 year of age. Using OsiriX® [...] Read more.
Limited data exist regarding the size and volume of adrenal glands in puppies; therefore, the present research aims to describe volumetric and traditional measurements of adrenal glands in computed tomography (CT) images of 26 dogs under 1 year of age. Using OsiriX®MD v9.0.1, the adrenal volume as well as adrenal length, and the height and width of the cranial and caudal poles were documented. The results were compared with groups based on age, weight at the time of examination, and the dogs’ adult size when patients were clinically re-evaluated after more than 12 months of age. The mean adrenal gland volumes were 0.50 cm3 for the left (range 0.08–1.29 cm3) and 0.41 cm3 for the right (range 0.03–1.10 cm3) adrenal gland. The results showed that older puppies had larger adrenal glands, although the difference did not reach statistical significance. The volume of the adrenal glands correlated positively with body weight and the patients’ adult size. The findings highlight the diagnostic potential of CT-based adrenal volumetry and two-dimensional measurements and support their use in refining reference values for young dogs. The strong correlation between adrenal size and body weight emphasizes the importance of weight-adjusted interpretation in clinical settings. Full article
(This article belongs to the Section Veterinary Internal Medicine)
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16 pages, 3467 KB  
Article
Coordination-Driven Rare Earth Fractionation in Kuliokite-(Y), (Y,HREE)4Al(SiO4)2(OH)2F5: A Crystal–Chemical Study
by Sergey V. Krivovichev, Victor N. Yakovenchuk, Olga F. Goychuk and Yakov A. Pakhomovsky
Minerals 2025, 15(10), 1064; https://doi.org/10.3390/min15101064 - 10 Oct 2025
Viewed by 113
Abstract
The crystal structure of kuliokite-(Y), Y4Al(SiO4)2(OH)2F5, has been re-investigated using the material from the type locality the Ploskaya Mt, Kola peninsula, Russian Arctic. It has been shown that in contrast to previous studies, [...] Read more.
The crystal structure of kuliokite-(Y), Y4Al(SiO4)2(OH)2F5, has been re-investigated using the material from the type locality the Ploskaya Mt, Kola peninsula, Russian Arctic. It has been shown that in contrast to previous studies, the mineral is monoclinic, Im, with a = 4.3213(1), b = 14.8123(6), c = 8.6857(3) Å, β = 102.872(4)°, and V = 541.99(3) Å3. The crystal structure was solved and refined to R1 = 0.030 on the basis of 3202 unique observed reflections. The average chemical composition determined by electron microprobe analysis is (Y2.96Yb0.49Er0.27Dy0.13Tm0.07Lu0.05Ho0.05Gd0.01Ca0.01)Σ4.04Al0.92Si2.04O8-[(OH)2.61F4.42]Σ7.03; the idealized formula is (Y,Yb,Er)4Al[SiO4]2(OH)2.5F4.5. The crystal structure of kuliokite-(Y) contains two symmetrically independent Y sites, Y1 and Y2, coordinated by eight and seven X anions, respectively (X = O, F). The coordination polyhedra can be described as a distorted square antiprism and a distorted pentagonal bipyramid, respectively. The refinement of site occupancies indicated that the mineral represents a rare case of HREE fractionation among two cation sites driven by their coordination numbers and geometry. In agreement with the lanthanide contraction, HREEs are selectively incorporated into the Y2 site with a smaller coordination number and tighter coordination environment. The strongest building unit of the structure is the [AlX2(SiO4)2] chain of corner-sharing AlX6 octahedra and SiO4 tetrahedra running along the a axis. The chains have their planes oriented parallel to (001). The Y atoms are located in between the chains, along with the F and (OH) anions, providing the three-dimensional integrity of the crystal structure. Each F anion is coordinated by three Y3+ cations to form planar (FY3)8+ triangles parallel to the (010) plane. The triangles share common edges to form [F2Y2]4+ chains parallel to the a axis. The analysis of second-neighbor coordination of Y sites allowed us to identify the structural topology of kuliokite-(Y) as the only case of the skd network in inorganic compounds, previously known in molecular structures only. The variety of anionic content in the mineral allows us to identify the potential existence of two other mineral species that can tentatively be named ‘fluorokuliokite-(Y)’ and ‘hydroxykuliokite-(Y)’. Full article
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11 pages, 211 KB  
Article
Social-Educational Work and the Role of Social Workers in Re-Education Facilities for Youth in Slovakia: A Qualitative Study
by Katarína Vanková
Soc. Sci. 2025, 14(10), 597; https://doi.org/10.3390/socsci14100597 - 9 Oct 2025
Viewed by 252
Abstract
The formation of an emotional bond with young people’s parents is crucial for healthy development and serves as a model for future relationships. Disruptions to this bond can result in neurobiological consequences, manifesting as problematic behaviours and social deficits. Social-educational work in re-education [...] Read more.
The formation of an emotional bond with young people’s parents is crucial for healthy development and serves as a model for future relationships. Disruptions to this bond can result in neurobiological consequences, manifesting as problematic behaviours and social deficits. Social-educational work in re-education facilities in Slovakia focuses on supporting and rehabilitating young people in conflict with the law, aiming for their reintegration into society. This study presents a qualitative analysis of social workers’ activities across 11 re-education facilities in Slovakia, utilizing semi-structured interviews, document analysis, and field observation. The findings reveal that social workers play an indispensable role in the re-education and resocialization process, providing emotional support, professional counselling, and coordination with multidisciplinary teams. Despite differences in client typology and methodologies among facilities, a shared emphasis exists on restoring social ties and personal development. The effectiveness of social work is influenced by adequate staffing, methodological support, and inter-ministerial cooperation. Implementing targeted recommendations could significantly enhance the effectiveness of the system and improve outcomes for children and young people in institutional care. This study contributes vital insights into how social workers must balance competing interests—such as individual therapeutic needs, institutional constraints, and family and community involvement—to successfully facilitate youth reintegration into society. Full article
(This article belongs to the Section Childhood and Youth Studies)
27 pages, 9738 KB  
Article
Machine Learning Recognition and Phase Velocity Estimation of Atmospheric Gravity Waves from OI 557.7 nm All-Sky Airglow Images
by Rady Mahmoud, Moataz Abdelwahab, Kazuo Shiokawa and Ayman Mahrous
AI 2025, 6(10), 262; https://doi.org/10.3390/ai6100262 - 7 Oct 2025
Viewed by 465
Abstract
Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual [...] Read more.
Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual inspection, where airglow images were collected from the OMTI network at Shigaraki (34.85 E, 134.11 N) from October 1998 to October 2002. Nonetheless, a large dataset of airglow images are processed and classified for studying AGW seasonal variation in the middle atmosphere. In this article, a machine learning-based approach for image recognition of AGWs from ASAIs is suggested. Consequently, three convolutional neural networks (CNNs), namely AlexNet, GoogLeNet, and ResNet-50, are considered. Out of 13,201 deviated images, 1192 very weak/unclear AGW signatures were eliminated during the quality control process. All networks were trained and tested by 12,007 classified images which approximately cover the maximum solar cycle during the time-period mentioned above. In the testing phase, AlexNet achieved the highest accuracy of 98.41%. Consequently, estimation of AGW zonal and meridional phase velocities in the mesosphere region by a cascade forward neural network (CFNN) is presented. The CFNN was trained and tested based on AGW and neutral wind data. AGW data were extracted from the classified AGW images by event and spectral methods, where wind data were extracted from the Horizontal Wind Model (HWM) as well as the middle and upper atmosphere radar in Shigaraki. As a result, the estimated phase velocities were determined with correlation coefficient (R) above 0.89 in all training and testing phases. Finally, a comparison with the existing studies confirms the accuracy of our proposed approaches in addition to AGW velocity forecasting. Full article
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23 pages, 2173 KB  
Article
Prototype-Enhanced Few-Shot Relation Extraction Method Based on Cluster Loss Optimization
by Shenyi Qian, Bowen Fu, Chao Liu, Songhe Jin, Tong Sun, Zhen Chen, Daiyi Li, Yifan Sun, Yibing Chen and Yuheng Li
Symmetry 2025, 17(10), 1673; https://doi.org/10.3390/sym17101673 - 7 Oct 2025
Viewed by 261
Abstract
The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation prototypes by relying on [...] Read more.
The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation prototypes by relying on the support set to assign labels to query samples, inherently leverages the symmetry between support and query processing. Although these methods have achieved remarkable results, they still face challenges such as the misjudging of noisy samples or outliers, as well as distinguishing semantic similarity relations. To address the aforementioned challenges, we propose a novel semantic enhanced prototype network, which can integrate the semantic information of relations more effectively to promote more expressive representations of instances and relation prototypes, so as to improve the performance of the few-shot RE. Firstly, we design a prompt encoder to uniformly process different prompt templates for instance and relation information, and then utilize the powerful semantic understanding and generation capabilities of large language models (LLMs) to obtain precise semantic representations of instances, their prototypes, and conceptual prototypes. Secondly, graph attention learning techniques are introduced to effectively extract specific-relation features between conceptual prototypes and isomorphic instances while maintaining structural symmetry. Meanwhile, a prototype-level contrastive learning strategy with bidirectional feature symmetry is proposed to predict query instances by integrating the interpretable features of conceptual prototypes and the intra-class shared features captured by instance prototypes. In addition, a clustering loss function was designed to guide the model to learn a discriminative metric space with improved relational symmetry, effectively improving the accuracy of the model’s relationship recognition. Finally, the experimental results on the FewRel1.0 and FewRel2.0 datasets show that the proposed approach delivers improved performance compared to existing advanced models in the task of few-shot RE. Full article
(This article belongs to the Section Computer)
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32 pages, 508 KB  
Article
The Reflections of Raa Haqi Cosmology in Dersim Folk Tales
by Ahmet Kerim Gültekin
Religions 2025, 16(10), 1274; https://doi.org/10.3390/rel16101274 - 6 Oct 2025
Viewed by 423
Abstract
This article illuminates the cosmology of Raa Haqi (often called Dersim Alevism or Kurdish Alevism), a rarely examined strand within Alevi Studies. Existing scholarship’s emphasis on identity politics and sparse ethnography has left Raa Haqi’s mythological and cosmological dimensions underexplored. This paper approaches [...] Read more.
This article illuminates the cosmology of Raa Haqi (often called Dersim Alevism or Kurdish Alevism), a rarely examined strand within Alevi Studies. Existing scholarship’s emphasis on identity politics and sparse ethnography has left Raa Haqi’s mythological and cosmological dimensions underexplored. This paper approaches Raa Haqi through a dual authority framework: (1) Ocak lineages and Ocak–talip relations—sustained by kinship institutions like kirvelik, musahiplik, and communal rites such as the cem—and (2) jiares, non-human agents from the Batın realm that manifest in Zahir as sacred places, objects, and animals. Methodologically, I conduct a close, motif-based reading of folktales compiled by Caner Canerik (2019, Dersim Masalları I), treating them as ethnographic windows into living theology. The analysis shows that tales encode core principles—rızalık (mutual consent), ikrar (vow), sır (the secret knowledge), fasting and calendrical rites, ritual kinship, and moral economies involving humans, animals, and Batın beings. Dreams, metamorphosis, and jiare-centered orientations structure time–space, ethics, and authority beyond the Ocak, including in individual re-sacralizations of objects and sites. I conclude that these narratives do not merely reflect belief; they actively transmit, test, and renew Raa Haqi’s cosmological order, offering Alevi Studies a theory-grounded, source-proximate account of Kurdish Alevi mythic thought. Full article
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Article
LMVMamba: A Hybrid U-Shape Mamba for Remote Sensing Segmentation with Adaptation Fine-Tuning
by Fan Li, Xiao Wang, Haochen Wang, Hamed Karimian, Juan Shi and Guozhen Zha
Remote Sens. 2025, 17(19), 3367; https://doi.org/10.3390/rs17193367 - 5 Oct 2025
Viewed by 525
Abstract
High-precision semantic segmentation of remote sensing imagery is crucial in geospatial analysis. It plays an immeasurable role in fields such as urban governance, environmental monitoring, and natural resource management. However, when confronted with complex objects (such as winding roads and dispersed buildings), existing [...] Read more.
High-precision semantic segmentation of remote sensing imagery is crucial in geospatial analysis. It plays an immeasurable role in fields such as urban governance, environmental monitoring, and natural resource management. However, when confronted with complex objects (such as winding roads and dispersed buildings), existing semantic segmentation methods still suffer from inadequate target recognition capabilities and multi-scale representation issues. This paper proposes a neural network model, LMVMamba (LoRA Multi-scale Vision Mamba), for semantic segmentation of remote sensing images. This model integrates the advantages of convolutional neural networks (CNNs), Transformers, and state-space models (Mamba) with a multi-scale feature fusion strategy. It simultaneously captures global contextual information and fine-grained local features. Specifically, in the encoder stage, the ResT Transformer serves as the backbone network, employing a LoRA fine-tuning strategy to effectively enhance model accuracy by training only the introduced low-rank matrix pairs. The extracted features are then passed to the decoder, where a U-shaped Mamba decoder is designed. In this stage, a Multi-Scale Post-processing Block (MPB) is introduced, consisting of depthwise separable convolutions and residual concatenation. This block effectively extracts multi-scale features and enhances local detail extraction after the VSS block. Additionally, a Local Enhancement and Fusion Attention Module (LAS) is added at the end of each decoder block. LAS integrates the SimAM attention mechanism, further enhancing the model’s multi-scale feature fusion capability and local detail segmentation capability. Through extensive comparative experiments, it was found that LMVMamba achieves superior performance on the OpenEarthMap dataset (mIoU 52.3%, OA 69.8%, mF1: 68.0%) and LoveDA (mIoU 67.9%, OA 80.3%, mF1: 80.5%) datasets. Ablation experiments validated the effectiveness of each module. The final results indicate that this model is highly suitable for high-precision land-cover classification tasks in remote sensing imagery. LMVMamba provides an effective solution for precise semantic segmentation of high-resolution remote sensing imagery. Full article
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