Search Results (3,870)

The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over fiber identification for composition purposes. This protocol can be invasive and destructive for the artifacts under study, time-consuming, and it often relies on personal expertise. In this preliminary study, an alternative, macroscopic approach is proposed, based on texture and surface textile characteristics, using low-magnification images and deep learning models. Under this scope, a publicly available, imbalanced textile image dataset was used to pretrain and evaluate six computer vision architectures (ResNet50, EfficientNetV2, ViT, ConvNeXt, Swin Transformer, and MaxViT). In addition to accuracy, energy efficiency and ecological footprint of the process were assessed using the CodeCarbon tool. The results indicate that two of the convolutional neural network models, Swin and EfficientNetV2, both deliver competitive accuracies together with low carbon emissions, in comparison to the transformer and hybrid models. This alternative, promising, sustainable, and non-invasive approach for textile classification demonstrates the feasibility of developing a custom, heritage-based image dataset. Full article

Vaccine adjuvants play a crucial role in the field of vaccinology, yet they remain one of the least developed and poorly characterized components of modern biomedical research. The limited availability of clinically approved adjuvants highlights the urgent need for new molecules with well-defined mechanisms and improved safety profiles. Hemocyanins, large copper-containing metalloglycoproteins found in mollusks, represent a unique class of natural immunomodulators. Hemocyanins serve as carrier proteins that help generate antibodies against peptides and hapten molecules. They also function as non-specific protein-based adjuvants (PBAs) in both experimental human and veterinary vaccines. Their mannose-rich N-glycans allow for multivalent binding to innate immune receptors, including C-type lectin receptors (e.g., MR, DC-SIGN) and Toll-like receptor 4 (TLR4), thereby activating both MyD88- and TRIF-dependent signaling pathways. Hemocyanins consistently favor Th1-skewed immune responses, which is a key characteristic of their adjuvant potential. Remarkably, their conformational stability supports slow intracellular degradation and facilitates dual routing through MHC-II and MHC-I pathways, thereby enhancing both CD4+ and CD8+ T-cell responses. Several hemocyanins are currently being utilized in biomedical research, including Keyhole limpet hemocyanin (KLH) from Megathura crenulata, along with those from other gastropods such as Concholepas concholepas (CCH), Fissurella latimarginata (FLH), Rapana venosa (RvH), and Helix pomatia (HpH), all of which display strong immunomodulatory properties, making them promising candidates as adjuvants for next-generation vaccines against infectious diseases and therapeutic immunotherapies for cancer. However, their structural complexity has posed challenges for their recombinant production, thus limiting their availability from natural sources. This reliance introduces variability, scalability issues, and challenges related to regulatory compliance. Future research should focus on defining the hemocyanin immunopeptidome and isolating minimal peptides that retain their adjuvant activity. Harnessing advances in structural biology, immunology, and machine learning will be critical in transforming hemocyanins into safe, reproducible, and versatile immunomodulators. This review highlights recent progress in understanding how hemocyanins modulate mammalian immunity through their unique structural features and highlights their potential implications as potent PBAs for vaccine development and other biomedical applications. By addressing the urgent need for novel immunostimulatory platforms, hemocyanins could significantly advance vaccine design and immunotherapy approaches. Full article

As a behavioral ability, flexibility plays an indispensable role in human learning activities. However, the analysis of flexibility in specific disciplines has not yet been fully explored. In response, through trigonometry of mathematics, this study investigated the strategic flexibility of high school level students, examining the influence of external factors such as gender and class on flexibility, and exploring the relationship between cognitive and strategic flexibility. Based on the four-stage flexibility test and the cognitive flexibility questionnaire survey of 237 11th-grade students in China, the current research yielded the following findings: (1) There is a positive correlation between potential strategic flexibility and actual strategic flexibility, and the actual strategic flexibility level of students is higher than that of potential strategic flexibility. (2) Gender and class have no significant relationship with strategic flexibility, but different subject combinations have a certain impact on flexibility. (3) Cognitive flexibility has a positive effect on both potential strategic flexibility and actual strategic flexibility. These findings have provided some research basis for understanding students’ external performance and adjusting teachers’ teaching behaviors, proposing a certain adjustment direction for the focus of teaching, students’ learning content, and classroom teaching methods. Full article

The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors. Full article

Knowledge Tracing aims to assess students’ mastery of knowledge concepts in real time, playing a crucial role in providing personalized learning services in intelligent tutoring systems. In recent years, researchers have attempted to introduce Neural Architecture Search (NAS) into knowledge tracing tasks to automatically design more efficient network structures. However, existing NAS-based methods for Knowledge Tracing suffer from excessively large search spaces and slow search efficiency, which significantly constrain their practical applications. To address these limitations, this paper proposes an Adaptive Neural Architecture Search framework based on Transformers for KT, called ANT-KT. Specifically, we design an enhanced encoder that combines convolution operations with state vectors to capture both local and global dependencies in students’ learning sequences. Moreover, an optimized decoder with a linear attention mechanism is introduced to improve the efficiency of modeling long-term student knowledge state evolution. We further propose an evolutionary NAS algorithm that incorporates a model optimization efficiency objective and a dynamic search space reduction strategy, enabling the discovery of high-performing yet computationally efficient architectures. Experimental results on two large-scale real-world datasets, EdNet and RAIEd2020, demonstrate that ANT-KT significantly reduces time costs across all stages of NAS while achieving performance improvements on multiple evaluation metrics, validating the efficiency and practicality of the proposed method. Full article

Depth completion aims to achieve high-quality dense depth prediction from a pair of synchronized sparse depth map and RGB image, and it plays an important role in many intelligent applications, including urban mapping, scene understanding, autonomous driving, and augmented reality. Although the existing convolutional neural network (CNN)-based deep learning architectures have obtained state-of-the-art depth completion results, depth ambiguities in large areas with extremely sparse depth measurements remain a challenge. To address this problem, an efficient hierarchical feature fusion network (HFF-Net) is proposed for producing complete and accurate depth completion results. The key components of HFF-Net are the hierarchical depth completion architecture for predicting a robust initial depth map, and the multi-level spatial propagation network (MLSPN) for progressively refining the predicted initial depth map in a coarse-to-fine manner to generate a high-quality depth completion result. Firstly, the hierarchical feature extraction subnetwork is adopted to extract multi-scale feature maps. Secondly, the hierarchical depth completion architecture that incorporates a hierarchical feature fusion module and a progressive depth rectification module is utilized to generate an accurate and reliable initial depth map. Finally, the MLSPN-based depth map refinement subnetwork is adopted, which progressively refines the initial depth map utilizing multi-level affinity weights to achieve a state-of-the-art depth completion result. Extensive experiments were undertaken on two widely used public datasets, i.e., the KITTI depth completion and NYUv2 datasets, to validate the performance of HFF-Net. The comprehensive experimental results indicate that HFF-Net produces robust depth completion results on both datasets. Full article

With the accelerating pace of societal digitalization, parental mediation practices have emerged as a critical mechanism for facilitating children’s socialization within the family system. Based on the 2022 Chinese Minors’ Digital Life and Online Protection Survey dataset, this study empirically examined the current state of parental mediation and its influencing mechanism on school-aged children’s academic performance. The results indicated that the level of parental mediation demonstrated a significantly positive influence on students’ academic performance. This finding remained robust when subjected to rigorous validation methods, including instrumental variable analysis, propensity score matching, and double machine learning techniques. Meanwhile, the mediation models showed that children’s e-learning behavior and civic engagement played parallel mediating roles in the relationship between parental mediation and academic performance. Moreover, heterogeneity analysis further revealed that family socioeconomic status negatively moderated the indirect pathways through which parental mediation influenced students’ academic performance. Our research suggested that active parental mediation could not only be beneficial for children’s academic performance but also be helpful to narrowing developmental inequalities across different social classes. Full article

In an increasingly data-driven world, sparking children’s curiosity for meaningful data exploration provides a powerful foundation for lifelong data literacy. Human–data interaction (HDI) offers a promising approach by making data more accessible and engaging, particularly in informal learning environments like museums. However, there is limited understanding of how children, as a distinct user group, engage with embodied, interactive data visualizations. This paper presents findings from an exploratory field study of a gesture-controlled HDI installation deployed in a large urban museum. We analyzed the interactions of over 200 children, primarily from visiting K-12 school groups, as they engaged with an HDI prototype, the data on display, and each other. Our thematic analysis reveals that children’s interactions are deeply social, playful, and imaginative, often prioritizing collaborative discovery and role-playing over direct data interpretation. Based on these observations, we present design recommendations for creating HDI installations that leverage these behaviors to foster meaningful data engagement. Full article

Background/Objectives: Endoscopic ultrasound (EUS) is crucial for lymph node (LN) characterization, playing a key role in oncological staging and treatment guidance. EUS criteria for predicting malignancy are imprecise, and histologic diagnosis may have limitations. This multicenter study aimed to evaluate the effectiveness of a novel artificial intelligence (AI)–based system in predicting LN malignancy from EUS images. Methods: This multicenter study included EUS images from nine centers. Lesions were labeled (“malignant” or “benign”) and delimited with bounding boxes. Definitive diagnoses were based on cytology/biopsy or surgical specimens and, if negative, a minimum six-month clinical follow-up. A convolutional neural network (CNN) was developed using the YOLO (You Only Look Once) architecture, incorporating both detection and classification modules. Results: A total of 59,992 images from 82 EUS procedures were analyzed. The CNN distinguished malignant from benign lymph nodes with a sensitivity of 98.8% (95% CI: 98.5–99.2%), specificity of 99.0% (95% CI: 98.3–99.7%), and precision of 99.0% (95% CI: 98.4–99.7%). The negative and positive predictive values for malignancy were 98.8% and 99.0%, respectively. Overall diagnostic accuracy was 98.3% (95% CI: 97.6–99.1%). Conclusions: This is the first study evaluating the performance of deep learning systems for LN assessment using EUS imaging. Our AI-powered imaging model shows excellent detection and classification capabilities, emphasizing its potential to provide a valuable tool to refine LN evaluation with EUS, ultimately supporting more tailored, efficient patient care. Full article

Higher technical education plays a strategic role in equipping the workforce to navigate rapid technological advancements and evolving labor market demands. Within the Rome Technopole framework, Spoke 4 targets ITS Academies, promoting the development of flexible, modular programs that integrate advanced technical skills with ethical, legal, and societal perspectives. This study reports on a pilot training initiative on Artificial Intelligence (AI) co-designed by the Istituto Superiore di Sanità (ISS), aimed at exploring the ethical, practical, and educational relevance of AI in higher technical education. The module was developed and tested through a multi-stakeholder collaboration involving educators, institutional actors, and learners. A four-phase approach was adopted: (1) initial stakeholder consultation to identify needs and content directions, (2) collaborative design of the training module, (3) online delivery and engagement using a CAWI-based focus group, and (4) mixed-method evaluation, combining quantitative assessments and open-ended qualitative feedback. This design facilitated asynchronous participation and encouraged critical reflection on the real-world implications of AI. Through the four-phase approach, the pilot module was developed, delivered, and assessed with 37 participants. Quantitative analysis revealed high ratings for clarity, relevance, and perceived utility in terms of employability. Qualitative feedback highlighted the interdisciplinary design, the integration of ethical reasoning, and the module’s broad applicability across sectors—particularly Healthcare and Industry. Participants suggested including more real-world case studies and collaborative learning activities to enhance engagement. The findings support the feasibility and added value of embedding ethically informed, interdisciplinary AI education in professional technical training pathways. Developed within the Rome Technopole ecosystem, the pilot module offers a promising approach to fostering critical digital literacy and preparing learners for responsible engagement with emerging technologies. Full article

Background: Innovative interventions, such as social media platforms and telemedicine, were implemented during the COVID-19 lockdown period for HIV prevention and treatment services. However, limited studies have reported on the facilitators and barriers of these innovations for HIV pre-exposure prophylaxis (PrEP) service continuity. Therefore, this study aimed to identify the barriers and facilitators of the implemented PrEP innovative interventions during COVID-19 among adolescent girls and young women (AGYW). Methods: A qualitative exploratory design was used to conduct semi-structured interviews with twelve stakeholders in the Dr Kenneth Kaunda District, North West Province of South Africa. Participants included various TB HIV Care programme stakeholders, comprising professional nurses, case managers, peer educators, and counsellors. The Consolidated Framework for Implementation Research (CFIR) 2.0 domains and constructs guided the interview questions and the analysis process. Additionally, all interviews were audio-taped, transcribed verbatim, and analyzed through thematic analysis. The facilitators and barriers of the PrEP innovative interventions were categorized according to the five CFIR domains. Results: The findings showed that despite the COVID-19 disruptions in healthcare services, the implemented innovative PrEP interventions enhanced the HIV prevention services. Facilitators included sufficient mobile data, teamwork, clear communication from managers, resilience, and existing media pages that supported social media-based PrEP service continuity. The implementation barriers included service users’ lack of cell phone devices, incorrect personal information, fear of contracting COVID-19, and limited individual movements. Conclusion: Social media and digital technologies played a crucial role in the continuation of HIV PrEP services among AGYW. These evaluations also illustrated the potential of social media platforms to be leveraged for HIV service delivery during periods of disruption, such as the COVID-19 lockdown period, for HIV service delivery. Furthermore, lessons learned from this study are significant and offer practical considerations for sustaining PrEP during service disruptions. Full article

Breast cancer is one of the leading causes of mortality among women globally, and an early and accurate diagnosis is essential for effective treatment and improved survival rates. Traditional diagnostic techniques often struggle to differentiate between benign and malignant tumors due to overlapping visual characteristics, resulting in false positives or delayed detection. For efficient breast cancer detection with machine learning, it is vital to identify the most significant features because those features play the most important roles in the treatment process. This study addresses this challenge by evaluating and comparing the performance of ten state-of-the-art machine learning classifiers for breast cancer detection using image-derived features. Initially, 30 features were extracted from a novel tertiary hospital dataset, and models were evaluated based on accuracy, precision, recall, and F-measure. To enhance model performance and reduce dimensionality, the Correlation-based Feature Selection (CFS) method was applied, leading to the identification of 11 highly informative features. Our experimental results demonstrate that, while models such as SVM and Logistic Regression achieved the highest accuracy (97.7%) on the full feature set, the Neural Network exhibited a superior performance (97.2%) on the reduced feature set, with a substantial reduction in training time. Most classifiers maintained comparable or improved accuracy with fewer features, indicating effective dimensionality reduction. Furthermore, pairwise statistical significance testing confirmed that ensemble and kernel-based classifiers achieved a statistically superior performance over simpler models. These findings highlight the importance of effective feature selection in developing accurate, efficient, and scalable breast cancer prediction systems. Full article

Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The platform supports full navigation in English and Spanish, with seamless language switching and both written and spoken input options. It incorporates automatic speech recognition (ASR) capable of handling code-switching, enhancing accessibility for bilingual users. Educational content is delivered through culturally sensitive videos organized into four categories: prevention, detection, diagnosis, and treatment. Each video includes embedded and post-video assessment questions aligned with Bloom’s Taxonomy to foster active learning. Users can monitor their progress and quiz performance via a personalized dashboard. An integrated chatbot, powered by large language models (LLMs), allows users to ask foundational breast cancer questions in natural language. The platform also recommends relevant resources, including nearby treatment centers, and support groups. LLMs are further used for ASR, question generation, and semantic response evaluation. Combining language technologies and LLMs reduces disparities in cancer education and supports informed decision-making among underserved populations, playing a pivotal role in reducing information gaps and promoting informed healthcare decisions. Full article

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive subtype of kidney cancer. This study aimed to construct a prognostic model for ccRCC based on glycosyltransferase genes, which play important roles in cell processes like proliferation, apoptosis. Glycosyltransferase genes were collected from four public databases and analyzed using RNA-seq data with clinical information from three ccRCC datasets. Prognostic models were constructed using eight machine learning algorithms, generating a total of 117 combinatorial algorithm models, and the StepCox[forward]+Ridge model with the highest predictive accuracy (C-index = 0.753) which selected and named the Glycosyltransferases Risk Score (GTRS) model. The GTRS effectively stratified patients into high- and low-risk groups with significantly different overall survival and maintained robust performance across TCGA, CPTAC, and E-MTAB1980 cohorts (AUC > 0.75). High-risk patients exhibited higher tumor mutational burden, immunosuppressive microenvironment, and poorer response to immunotherapy. TYMP and GCNT4 were experimentally validated as key genes, functioning as oncogenic and tumor-suppressive factors. In conclusion, GTRS serves as a reliable prognostic tool for ccRCC and provides mechanistic insights into glycosylation-related tumor progression. Full article

Accurate precipitation forecasting plays a crucial role in sustainable water resource management, especially in arid regions like Konya, one of Turkey’s driest areas. Reliable forecasts support effective water budgeting, agricultural planning, and climate adaptation efforts in the region. This study investigates the performance of different deep learning training algorithms in forecasting monthly precipitation using Long Short-Term Memory (LSTM) networks, a method tailored for time-series prediction. A comprehensive dataset comprising 39 years (1984–2022) of precipitation records was utilized, obtained from the Turkish State Meteorological Service (MGM) as ground-based observations and from NASA’s POWER database as remote sensing data, and was split into 80% for training and 20% for testing. A comparative analysis of three widely used optimization algorithms, Adaptive Moment Estimation (ADAM), Root Mean Square Propagation (RMSProp), and Stochastic Gradient Descent with Momentum (SGDM), revealed that ADAM consistently outperformed the others in forecasting accuracy. Model performance was evaluated with statistical metrics, and the LSTM-ADAM combination achieved the best results. In the final phase, cross-validation was applied using MGM and NASA data sources in a crosswise manner to test model generalizability and data source independence. The best performance was observed when the model was trained with MGM data and tested with NASA data, achieving a remarkably low RMSE of 3.62 mm, MAE of 2.93 mm, R² of 0.9966, and NSE of 0.9686. When trained with NASA data and tested with MGM data, the model still demonstrated strong performance, with an RMSE of 4.48 mm, MAE of 3.22 mm, R² of 0.9921, and NSE of 0.9678. These results demonstrate that satellite and ground-based data can be used interchangeably under suitable conditions, while also confirming the superiority of the ADAM optimizer in LSTM-based precipitation forecasting. Full article