Search Results (1,117)

This article conceptualizes active tourism as a strategy for promoting physical activity (PA) among children and adolescents and examines the literature that has analyzed its different modalities and their application across diverse settings and contexts. A systematic review (2015–2025) was conducted in accordance with PRISMA 2020, with searches performed in PubMed, Scopus, and Web of Science. Predefined inclusion and exclusion criteria were applied, alongside rigorous screening procedures and methodological quality assessment. Twelve studies were included, covering experiential and knowledge-oriented modalities implemented in curricular, extracurricular, family, and community contexts. The results show that active tourism increases PA frequency, duration, and intensity, and enhances physical fitness indicators as well as psychosocial variables (intrinsic motivation, enjoyment, autonomy, and competence). Experiential modalities and rural/natural environments predominate, generally yielding stronger effects than urban or mixed settings; however, these latter contexts broaden reach and equity by integrating activities into daily routines. Conceptual heterogeneity and the scarcity of longitudinal studies limit the estimation of sustained effects and the comparison across modalities. At present, active tourism emerges as a transversal approach to promoting meaningful PA in children and adolescents, integrating movement, learning, and well-being. Comparative and longitudinal designs capable of quantifying dose–response patterns by modality and setting are recommended, as well as policies that strengthen school–family–community linkages to enhance adherence and reduce inequalities in access to active opportunities. Full article

In the rapid diffusion of artificial intelligence (AI), firms increasingly rely on AI to reshape user interactions, yet how such interactions translate into sustained innovation remains unclear. Adopting a user–producer interaction perspective, this study examines how AI-Driven User–Producer Interaction (ADUPI) affects User–Producer Interconnected Innovation (UPII), focusing on the mediating roles of User–Producer Knowledge Exchange (UPKE) and User–Producer Knowledge Integration (UPKI), as well as the moderating effect of AI Readiness (AIR). Using survey data from 974 firms and applying regression, mediation, moderation, and bootstrap analyses, the findings show that ADUPI significantly enhances UPII. Moreover, UPKE and UPKI jointly mediate this relationship, forming a dual mediation mechanism in which knowledge integration exerts a stronger effect than knowledge exchange. In addition, AIR positively moderates the effects of ADUPI on both UPKE and UPKI, amplifying innovation outcomes under higher AI readiness. This study advances AI and innovation research by shifting the focus from internal firm capabilities to cross-actor interaction, clarifying differentiated knowledge mechanisms, and highlighting AI readiness as a key condition for value realization. The results also provide actionable insights for firms seeking to convert AI-driven interaction into interconnected innovation through improved AI readiness and knowledge management. Full article

Accurate forecasting of aggregated demand response (DR) potential is critical for load aggregators, yet remains challenging under severe data scarcity and domain shift conditions. This paper proposes a domain-adaptive transfer learning framework based on an ensemble of Random Vector Functional-Link (RVFL) neural networks for DR potential prediction without requiring any labeled target-domain data. By integrating domain adaptation layers and Maximum Mean Discrepancy (MMD) regularization, the proposed method explicitly reduces marginal feature distribution discrepancies between source and target domains, enabling effective knowledge transfer across heterogeneous operating scenarios. Compared with deep learning architectures, the RVFL-based framework offers favorable theoretical and practical properties for this application, including closed-form least-squares training, reduced risk of overfitting under limited data, and stable generalization under distribution shifts due to its direct-link structure and randomized hidden representations. These characteristics lead to significantly lower computational complexity and training cost than gradient-based deep models, while maintaining strong predictive capability. Case studies using real-world residential consumption data from the Pecan Street dataset demonstrate that the proposed approach consistently outperforms benchmark methods, including SVR, RF, and LSTM, across both intra-year and cross-year transfer scenarios. Reliable prediction accuracy is achieved even when only 10% of source-domain data are available, indicating strong data efficiency and scalability for practical aggregator deployment in day-ahead DR planning. Full article

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems. Full article

Large language models (LLMs) with the Transformer architecture as the core have made significant progress in the field of natural language processing, and their application value in English teaching has also attracted much attention. In tasks such as text generation, question-answering systems, and translation, the processing capabilities of LLMs have significantly improved. However, existing LLMs have problems such as insufficient coverage of professional knowledge, rough semantic parsing, and weak personalized services. To address the aforementioned issues, this study proposes a dual-path retrieval-enhanced generation scheme that integrates vector databases and intelligent agents, aiming to improve the application of large models in English language teaching. Semantic retrieval of unstructured data in English teaching is realized through vector databases, knowledge is dynamically acquired by combining agents, and the accuracy is improved by using Bloom filters to fuse dual-path retrieval. At the same time, the retrieval efficiency is optimized by an importance-oriented algorithm, and user profiles are constructed based on multi-dimensional data to achieve personalized adaptation. Experiments show that the maximum optimization of the retrieval time of this scheme can reach 26.32%, and the highest retrieval accuracy can reach 86%. The key indicators and scores in tasks such as English knowledge retrieval and question-answering reasoning are better than those of the comparative schemes, providing an effective technical path for intelligent English teaching. Full article

This study develops a micro-ring resonator that provides optical amplification based on NaYF₄:5%Er³⁺ nanoparticles doped with SU-8. By utilizing the frequency selection properties of the micro-ring resonator, a filter with amplification capabilities is successfully developed. The device features a quality factor of 5.72 × 10⁴ and a free spectral range of 0.081 nm. Operating at an on-chip power of 108 mW, the micro-ring resonator amplifier exhibits a relative gain of 8.92 dB within a size of 2.3 cm × 1.5 cm. To the best of our knowledge, the amplification of optical signals in micro-ring resonators using erbium-doped polymers has not been reported. This technology highlights the significant potential of using erbium-doped materials to fabricate various integrated devices for on-chip optical amplification. Full article

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article

This project presents the development of a Retrieval-Augmented Generation (RAG) system applied to the customization of a Non-Playable Character (NPC), designed as the Non-Playable Character (NPC) of the President of the IDIS Foundation Città della Scienza (City of Science). The NPC acts as both a virtual guide and institutional ambassador within the science center, providing multilingual, interactive, and accessible communication for a broad international audience. Through the integration of generative models with a curated, validated knowledge base, the RAG system enables the NPC to provide accurate, context-sensitive, and up-to-date responses to user queries. Developed by the Teaching Learning Centre for Education and Inclusive Technologies ‘Elisa Frauenfelder’ at the University of Salerno, the system supports the museum’s educational mission by enhancing science communication and fostering inclusive digital engagement. The Non-Playable Character (NPC) features realistic facial animation, movement, and voice synthesis, creating a digital twin capable of simulating human-like interaction. This initiative exemplifies an innovative application of artificial intelligence for an inclusive and equitable quality education and contributes to the development of engaging, accessible, and personalized learning environments. Full article

Destructive earthquakes frequently sever transportation lifelines, significantly impeding the progress of emergency rescue and post-disaster reconstruction efforts. The automated identification of road damage utilizing high-resolution remote sensing imagery is strictly constrained by the scarcity of post-disaster labeled samples and the morphological complexity of road networks. Consequently, model segmentation results frequently suffer from discontinuities in topological connectivity and confusion between background features and damaged roads. To address these challenges, this study proposes a road damage detection framework that integrates generative artificial intelligence with vector prior knowledge. A data simulation pipeline utilizing a stable diffusion model was constructed, employing topologically constrained masking to generate high-fidelity synthetic damage samples based on the DeepGlobe dataset, thereby mitigating the data deficit. The proposed Vector-Guided Damaged Road Segmentation Network (VRD-U2Net) employs wavelet convolutions (WTConv) to decouple high-frequency noise from low-frequency structural components and utilizes a Multi-Scale Residual Attention (MSRA) module to align visual features with vector priors. Furthermore, a vector-prior-driven dynamic upsampling mechanism is introduced to enforce geometric constraints on model predictions. Experimental results demonstrate that the method achieves an mIoU of 0.884 on the synthetic dataset. In validation using real-world imagery from the 2023 Turkey earthquake, the model attained an F1-score of 65.3% and recall of 72.3% without fine-tuning, exhibiting robust generalization capabilities to support manual damage assessment in data-scarce emergency scenarios. Full article

With the growth of large language models (LLMs), there are increasing calls to interpret their behavior through the prism of analogies to human cognitive mechanisms. At the same time, scientific literature points to the fundamental limitations of these systems, describing them, among other things, as models that generate a superficial simulation of reasoning without real access to semantic meanings (“stochastic parrots” or “illusion of reasoning”). This paper proposes an innovative, modular benchmark for assessing the cognitive competence of LLMs, integrating three complementary dimensions of language processing: factual, syntactic, and logical. Eight language models (LLama 3.2, Mistral 7B, LLama 3:8B, Gemini 2.5 Flash, ChatGPT-3, ChatGPT-4o mini, ChatGPT-4, and ChatGPT-5) were tested using a uniform procedure with context reset after each interaction and a three-point scoring scheme (0/0.5/1). The results obtained showed a clear advantage for the largest models in tasks based on general knowledge and formal transformations known from training, with a significant decrease in effectiveness, regardless of model size, in tasks requiring conjunctive reasoning based solely on new, local premises. Importantly, unstable but measurable corrective abilities of some models were also observed after feedback, suggesting the presence of reactive mechanisms, but were insufficient to consider them systems capable of cognitive self-reflection. The combined analysis indicates that LLMs effectively simulate syntax and logic rules when the task corresponds to recognizable formal patterns, but fail in situations requiring the construction of new, coherent chains of beliefs and symbolic inferences, which undermines the thesis of their cognitive “understanding”. The results justify the need to create more complex and semantically restrictive evaluation frameworks that will allow distinguishing statistical fit from systemic, multi-stage formal reasoning. The proposed benchmark is a step towards a more multidimensional and diagnostic evaluation of LLMs, shifting the focus from “will the model respond correctly?” to “why and under what conditions is the model able to reason?” Full article

Accurate identification of building structural types (BSTs) is essential for seismic vulnerability assessment and disaster risk management. Traditional field survey methods are constrained by high costs, low efficiency, and limited scalability. Although remote sensing-based approaches offer strong potential for large area applications, they are often hindered by limited spatial resolution, spectral confusion, and difficulties in capturing information related to internal building structures. To address these limitations, this study proposes a BST classification approach that integrates remote sensing image features with multisource prior knowledge. In addition to conventional remote sensing features derived from building shape, spectral, and texture, multiple types of prior information are incorporated to compensate for the insufficient structural discriminative capability of remote sensing imagery alone. These include distance to roads, terrain conditions, building height, population, gross domestic product (GDP), and nighttime light intensity. Considering the limited number of labeled samples and the high dimensionality of features, fourteen mainstream machine learning algorithms are systematically evaluated. Through feature selection and model optimization, XGBoost is identified as the most effective classifier, achieving the highest weighted F1 score of 78.62%. The results demonstrate that, under the same machine learning model settings, models trained solely on remote sensing features consistently underperform those integrating multisource features combined with feature selection, confirming the effectiveness of synergistically fusing remote sensing features with prior knowledge for improving overall BST classification performance. Further analyses demonstrate that different groups of remote sensing features and prior knowledge are associated with reductions in misclassification between specific BSTs. Compared with approaches based exclusively on remote sensing imagery, the proposed method exhibits higher and more balanced classification performance across different BSTs, with particularly notable advantages for structure categories that are difficult to distinguish using single-source remote sensing features. This study provides the foundation for subsequent seismic vulnerability analysis and related risk studies. Full article

Hosts generating unsolicited network traffic increasingly operate in a coordinated manner rather than in isolation. Scanning and exploitation activities are often distributed across multiple hosts that share common infrastructure, toolchains, and behavioral patterns, forming loosely coupled yet persistently aligned sender groups. Accurately attributing such groups is critical for understanding organized activities and strengthening network defense capabilities. However, existing attribution approaches face notable limitations. Methods that rely on threat intelligence suffer from delayed updates and limited coverage. Static feature-based approaches ignore temporal ordering and therefore fail to capture multi-stage behavioral evolution. Although dynamic sequence models incorporate temporal patterns, they typically overlook the collaborative structural relationships among coordinated senders. In this paper, we propose SentinelGraph, a temporal graph reasoning framework for sender group attribution from honeypot traffic. SentinelGraph constructs a temporal knowledge graph and integrates a recurrent graph evolution module to jointly model coordination structures and their temporal dynamics. A structure enhancement module further exploits contextual information available at the target time, while an auxiliary relation loss encourages the learning of enriched entity representations. This design enables accurate attribution even for previously unseen senders by leveraging information from their observed neighbors. Experiments on real-world honeypot data demonstrate that SentinelGraph substantially outperforms state-of-the-art methods in modeling coordinated network behaviors. Full article

Data science has become central to contemporary social, civic, and professional life, yet its integration into initial teacher education remains fragmented and undertheorised. This paper addresses the need to support teacher educators in designing learning experiences that develop pre-service teachers, who are non-data science specialists, competence in data science. A systematic scoping review of the literature was conducted across major academic databases and complemented by an expert-informed literature identification strategy. The review examined how data science is described conceptually, how it is structured within school curricula and teacher education, and what knowledge and practices are emphasised for teachers. Findings indicate that while core processes and practices of data science, such as problem formulation, data preparation, exploratory analysis, modelling, visualisation, and ethical engagement, are widely recognised, their translation into teacher education is inconsistent and often lacks coherence. In response, the paper presents a conceptual framework designed to support pre-service teachers in engaging with the processes and practices of doing data science. The framework offers a flexible, practice-informed structure that is accessible to non-specialist teachers and aligned with pedagogical decision-making in educational settings. The paper concludes by discussing how the framework, alongside practical considerations for enactment, can support the preparation of data-literate teachers capable of fostering critical, ethical, and inquiry-based engagements with data in schools. Full article

Misdiagnosing plant diseases often leads to a range of negative consequences, including the overuse of pesticides and unnecessary food waste. Traditionally, identifying diseases on plant leaves has relied on manual visual inspection, making it a complex and time-consuming task. Since the advent of convolutional neural networks, however, recognition performance for leaf diseases has improved significantly. Most contemporary studies that apply AI techniques to plant-leaf disease classification focus primarily on boosting accuracy, frequently overlooking the limitations posed by resource-constrained real-world environments. To address these challenges, this thesis employs knowledge distillation to enable small models to approximate the recognition capabilities of larger ones. We enhance a ShuffleNetV2-based model by integrating multi-scale feature extraction and a coordinate-attention mechanism, and we further improve the lightweight student model through knowledge distillation to boost its recognition performance. Experimental results show that the proposed model achieves 93.15% accuracy on the Plant Pathology 2021- FGVC8 dataset, utilizing only 0.36 M parameters and 0.0931 GFLOPs. Compared to the ResNet50 baseline, our architecture slashes parameters by nearly 98% while limiting the accuracy gap to a mere 1.6%. These results confirm the model’s ability to maintain robust performance with minimal computational overhead, providing a practical solution for precision agriculture on resource-limited edge devices. Full article

Tomato leaf diseases exhibit subtle inter-class differences and substantial intra-class variability, making accurate identification challenging for conventional deep learning models, especially under real-world conditions with diverse lighting, occlusion, and growth stages. Moreover, most existing approaches rely solely on visual features and lack the ability to incorporate semantic descriptions or expert knowledge, limiting their robustness and interpretability. To address these issues, we propose LPDiag, a multimodal prototype-attention diagnostic framework that integrates large language models (LLMs) for fine-grained recognition of tomato diseases. The framework first employs an LLM-driven semantic understanding module to encode symptom-aware textual embeddings from disease descriptions. These embeddings are then aligned with multi-scale visual features extracted by an enhanced Res2Net backbone, enabling cross-modal representation learning. A set of learnable prototype vectors, combined with a knowledge-enhanced attention mechanism, further strengthens the interaction between visual patterns and LLM prior knowledge, resulting in more discriminative and interpretable representations. Additionally, we develop an interactive diagnostic system that supports natural-language querying and image-based identification, facilitating practical deployment in heterogeneous agricultural environments. Extensive experiments on three widely used datasets demonstrate that LPDiag achieves a mean accuracy of 98.83%, outperforming state-of-the-art models while offering improved explanatory capability. The proposed framework offers a promising direction for integrating LLM-based semantic reasoning with visual perception to enhance intelligent and trustworthy plant disease diagnostics. Full article