Search Results (3,602)

The present conceptual paper proposes a purpose-driven experiential marketing framework for film-induced destinations, integrating sustainability and emotional engagement into destination management. The model under discussion comprises five interconnected dimensions, namely integrated experience, branding, people, emotional touchpoints and processes. These are articulated through purpose-driven marketing principles and aligned with selected Global Reporting Initiative (GRI) indicators. This approach positions sustainability as an inherent component of value creation rather than an external policy layer. The framework under discussion was developed through an interdisciplinary literature review and is illustrated through insights from an exploratory case study of Monsanto, a rural Portuguese village recently featured in HBO’s House of the Dragon. Semi-structured interviews were conducted with a purposive sample of local stakeholders, including tourists, residents, entrepreneurs and institutional representatives. These interviews were analysed thematically to provide indicative evidence of the framework’s relevance and potential applicability. The findings suggest that emotional engagement, co-creation and territorial authenticity play a central role in shaping memorable film-related tourism experiences that are consistent with destination purpose and stakeholder well-being. The study also emphasises the strategic importance of storytelling, audiovisual narratives and collaborative governance in the strengthening of place identity and the support of sustainable differentiation. Despite its exploratory nature, the framework provides practical guidance for destination management organisations (DMOs), cultural programmers and creative industry actors. The article concludes by identifying avenues for future research, including cross-regional application, digital experimentation and the quantitative assessment of experiential dimensions. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Effective and transparent medical diagnosis relies on accurate and interpretable classification of medical images across multiple modalities. This paper introduces an explainable multi-modal image analysis framework based on a dual-stream architecture that fuses handcrafted descriptors with deep features extracted from a custom MobileNet. Handcrafted descriptors include frequency-domain and texture features, while deep features are summarized using 26 statistical metrics to enhance interpretability. In the fusion stage, complementary features are combined at both the feature and decision levels. Decision-level integration combines calibrated soft voting, weighted voting, and stacking ensembles with optimized classifiers, including decision trees, random forests, gradient boosting, and logistic regression. To further refine performance, a hybrid class-specific feature selection strategy is proposed, combining mutual information, recursive elimination, and random forest importance to select the most discriminative features for each class. This hybrid selection approach eliminates redundancy, improves computational efficiency, and ensures robust classification. Explainability is provided through Local Interpretable Model-Agnostic Explanations, which offer transparent details about the ensemble model’s predictions and link influential handcrafted features to clinically meaningful image characteristics. The framework is validated on three benchmark datasets, i.e., BTTypes (brain MRI), Ultrasound Breast Images, and ACRIMA Retinal Fundus Images, demonstrating generalizability across modalities (MRI, ultrasound, retinal fundus) and disease categories (brain tumor, breast cancer, glaucoma). Full article

Recent advances in satellite observations have expanded the use of Sea Surface Temperature (SST) and Sea Surface Height (SSH) data in climate and oceanography, yet their low spatial resolution limits fine-scale analyses. We propose HiT_DS, a modular hierarchical Transformer framework for high-resolution downscaling of SST and SSH fields. To address challenges in multiscale feature representation and physical consistency, HiT_DS integrates three key modules: (1) Enhanced Dual Feature Extraction (E-DFE), which employs depth-wise separable convolutions to improve local feature modeling efficiently; (2) Gradient-Aware Attention (GA), which emphasizes dynamically important high-gradient structures such as oceanic fronts; and (3) Physics-Informed Loss Functions, which promote physical realism and dynamical consistency in the reconstructed fields. Experiments across two dynamically distinct oceanic regions demonstrate that HiT_DS achieves improved reconstruction accuracy and enhanced physical fidelity, with selective module combinations tailored to regional dynamical conditions. This framework provides an effective and extensible approach for oceanographic data downscaling. Full article

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies. Full article

Inflammatory Bowel Diseases (IBD), which include Crohn’s disease (CD) and ulcerative colitis (UC), are chronic, immune-mediated disorders marked by persistent and recurrent inflammation of the gastrointestinal tract. Over the past two decades, major advances in understanding the immunologic and molecular pathways that drive intestinal injury have transformed the therapeutic landscape. This progress has enabled the development of novel biologics and small-molecule agents that more precisely target dysregulated immune responses, thereby improving clinical outcomes and quality of life for many patients. Despite these therapeutic advances, IBD remains a highly heterogeneous condition. Patients differ widely in disease phenotype, progression, and response to specific treatments. Consequently, selecting the most effective therapy for an individual patient requires careful consideration of clinical features, molecular markers, and prior treatment history. The shift toward personalized, prediction-based treatment strategies aims to optimize the timing and choice of therapy, minimize unnecessary exposure to ineffective drugs, and ultimately alter the natural course of disease. In this review, we provide a comprehensive overview of current evidence guiding drug positioning in IBD, with particular emphasis on biologic therapies and small-molecule inhibitors. We also examine emerging biomarkers, clinical predictors of response, and real-world factors that influence therapeutic decision-making. Finally, we discuss the challenges and limitations that continue to hinder widespread implementation of personalized strategies, underscoring the need for further research to integrate precision medicine into routine IBD care. Full article

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R² of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems. Full article

Prostate cancer remains the most frequently diagnosed malignancy in men and a leading cause of cancer-related mortality. Multiparametric MRI (mpMRI) has become the gold standard for non-invasive diagnosis, staging, and follow-up. Yet, its widespread adoption is hampered by long acquisition times, inter-reader variability, and interpretative complexity. Though most papers focus on specific applications without offering a cohesive therapeutic perspective, artificial intelligence (AI) has recently attracted attention as a potential solution to these shortcomings. For instance, deep learning models can help optimize imaging protocols for biparametric and multiparametric MRI, and AI-based reconstruction techniques have shown promise for reducing acquisition times without sacrificing diagnostic performance. Several systems have produced outcomes in the diagnostic phase that are comparable to those of skilled radiologists, as demonstrated in multicenter settings such as PI-CAI. Radiomics and radiogenomics provide more detailed insights into the biology of the disease by extracting quantitative features associated with tumor aggressiveness, extracapsular expansion, and treatment response, in addition to detection. Despite these developments, methodological variability, a lack of multicenter validation, proprietary algorithms, and unresolved standardization and governance difficulties continue to restrict clinical translation. Our work emphasizes the maturity of existing technologies, ongoing gaps, and the progressive integration necessary for successful clinical adoption by presenting AI applications aligned with the patient pathway. In this context, this review aims to outline how AI can support the entire patient journey—from acquisition and protocol selection to detection, quantitative analysis, treatment assessment, and follow-up—while maintaining a clinically centered perspective that emphasizes practical relevance over theoretical discussion, potentially enabling more reliable, effective, and customized patient care in the field of prostate cancer. Full article

Endometrial lesions are common in gynecology, exhibiting considerable clinical heterogeneity across different subtypes. Although ultrasound imaging is the preferred diagnostic modality due to its noninvasive, accessible, and cost-effective nature, its diagnostic performance remains highly operator-dependent, leading to subjectivity and inconsistent results. To address these limitations, this study proposes a hierarchical feature selection and optimization framework for endometrial lesions, aiming to enhance the objectivity and robustness of ultrasound-based diagnosis. Firstly, Kernel Principal Component Analysis (KPCA) is employed for nonlinear dimensionality reduction, retaining the top 1000 principal components. Secondly, an ensemble of three filter-based methods—information gain, chi-square test, and symmetrical uncertainty—is integrated to rank and fuse features, followed by thresholding with Maximum Scatter Difference Linear Discriminant Analysis (MSDLDA) for preliminary feature selection. Finally, the Whale Migration Algorithm (WMA) is applied to population-based feature optimization and classifier training under the constraints of a Support Vector Machine (SVM) and a macro-averaged F1 score. Experimental results demonstrate that the proposed closed-loop pipeline of “kernel reduction—filter fusion—threshold pruning—intelligent optimization—robust classification” effectively balances nonlinear structure preservation, feature redundancy control, and model generalization, providing an interpretable, reproducible, and efficient solution for intelligent diagnosis in small- to medium-scale medical imaging datasets. Full article

Accurate and quantitative detection of boar sperm heads is essential for breeding selection and reproductive management. Manual microscopic counting is time-consuming, labor-intensive, and prone to subjective bias, while existing computer-based algorithms often struggle to recognize sperm cells accurately when they overlap or move rapidly in high-magnification microscopic images. This study proposes a lightweight boar sperm detection model, YOLO11_SRP, designed to improve small-object recognition in complex microscopic scenarios. The model integrates a lightweight StarNet backbone, a rectangular self-calibration module for enhanced spatial feature modeling, and an additional low-level detection layer optimized for tiny targets. We evaluated the model on a boar sperm microscopic image dataset and compared it with the standard YOLO11s framework. The results show that YOLO11_SRP achieves an mAP@0.5 of 91.9%, representing a 13.9% improvement over YOLO11s, while simultaneously reducing parameters by 39% and computational cost by 14.1%. These findings demonstrate that YOLO11_SRP provides efficient and accurate sperm detection, supporting the development of efficient and reliable automated sperm analysis pipelines, in which sperm head detection serves as a fundamental preprocessing step. Full article

Background: Wound healing contributes to restoring skin integrity. However, scars affect soft tissue in all its layers, including the superficial and deep fascia; moreover, it has been demonstrated that the fibroblasts leading the scarring process develop from progenitors located in the superficial fascia. In the past, research into scar etiology has focused primarily on the dermal and epidermal layers, leaving the role of the fasciae largely overlooked. Many patients presenting with surgical or traumatic scars complain of the increased stiffness and thickness of the scar, reduced extensibility of the area surrounding it, and chronic pain persisting even after the healing process has been completed. The purpose of this systematic review is to investigate the non-invasive tools and methods employed for the objective evaluation of scars that involve fascial layers. Methods: A systematic literature search was conducted on PubMed and WOS. Registration DOI: 10.17605/OSF.IO/SDR3Q. Results: A total of 11 articles were selected; the etiologies of scars were surgical, traumatic, and other (keloids). The investigations were conducted using ultrasound, magnetic resonance imaging, strain elastography, and shear wave elastography on the visceral fasciae, superficial fascia, hypodermis, and musculoskeletal fasciae. Sliding of fasciae was assessed by ultrasound; thickness of fasciae was assessed by ultrasound and magnetic resonance imaging; stiffness was assessed by shear wave elastography and strain elastography; and the qualitative assessment was performed via ultrasound. Conclusions: Our literature review showed that ultrasound, magnetic resonance imaging, strain elastography, and shear wave elastography are currently adopted for investigating the sliding, thickness, stiffness, and qualitative features of scars involving fascial layers. Moreover, our research showed the existence of a gap in the scientific literature on this topic. Full article

Background/Objectives: Acute generalized exanthematous pustulosis (AGEP) is a severe drug-induced cutaneous reaction characterized by the abrupt onset of sterile pustules, fever, neutrophilia, and a T cell-mediated type IVd hypersensitivity response. This narrative review synthesizes current evidence on pharmacological triggers, immunopathogenic mechanisms, diagnostic criteria, and differential diagnosis to provide a clinically oriented framework. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, ScienceDirect, and SpringerLink for studies published between 2000 and 2025, complemented by selected clinical reference sources. Studies addressing clinical features, immunological pathways, pharmacovigilance signals, and diagnostic tools for AGEP were included. Synthesis of Evidence: β-lactam antibiotics remain the most frequent triggers, while increasing associations have been reported with hydroxychloroquine, targeted therapies, immune checkpoint inhibitors, psychotropic agents, and vaccines. Immunopathogenesis is driven by IL-36 activation, CXCL8/IL-8–mediated neutrophil recruitment, and IL36RN mutations, explaining overlap with pustular psoriasis. Diagnostic accuracy improves through integration of drug latency, clinical morphology, histopathology, biomarkers, and standardized tools such as the EuroSCAR score. Conclusions: AGEP is a complex pustular reaction induced by diverse drugs and amplified by IL-36-mediated inflammation. Accurate diagnosis requires a multidimensional approach supported by structured algorithms and robust pharmacovigilance to identify evolving drug-associated patterns. Full article

Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this study, we developed a multi-stage natural language processing (NLP) pipeline to automate knowledge extraction, organization, and integration from the agricultural research literature into a domain-consistent crop disease knowledge graph. The model combines transformer-based sentence embeddings with variational deep clustering to extract topics, which are further refined via facet-aware relevance scoring for sentence selection to be included in the summary. Lexicon-guided named entity recognition helps in the precise identification and normalization of terms for crops, diseases, symptoms, etc. Relation extraction based on a combination of lexical, semantic, and contextual features leads to the meaningful generation of triplets for the knowledge graph. The experimental results show that the method yielded consistently good results at each stage of the knowledge extraction process. Among the combinations of embedding and deep clustering methods, SciBERT + VaDE achieved the best clustering results. The extraction of representative sentences for disease symptoms, control/treatment, and prevention obtained high F1-scores of around 0.8. The resulting knowledge graph has high node coverage and high relation completeness, as well as high precision and recall in triplet generation. The multi-stage NLP pipeline effectively converts unstructured agricultural research texts into a coherent and semantically rich knowledge graph, providing a basis for further research in crop disease analysis, knowledge retrieval, and data-driven decision support in agricultural informatics. Full article

In educational settings, it is crucial to comprehend and manage individuals’ social interaction behaviors through the physical environment. However, analyzing social interaction patterns manually is a time-consuming and energy-intensive process. This study aims to reveal the socio-behavioral implications of spatial features, based on the Affordance Theory, using artificial intelligence (AI). To this end, the study proposes a unified quantitative methodology that leverages diverse AI approaches. Behavioral data are gathered via systematic observation and analyzed using (1) Deep Learning (DL)-based Human Detection and classified by (2) Machine Learning (ML)-based Interaction Score Prediction approach. The behavioral findings were analyzed in relation to spatial data via (3) Spatial Feature Selection. As the study area, the ATU Faculty of Engineering building complex was selected, and behavioral data from 746 participants were collected in the complex’s open spaces. The results indicated that AI-based approaches provide a high degree of precision in analyzing the relationships between social interaction and spatial features within the addressed context. Also, (1) the existence and (2) the rotation of seating units and (3) shading strategies are identified as the spatial features that contribute to higher interaction scores in the educational settings. The study proposes an integrated and transferable methodology based on diverse AI approaches for determining social interaction and its spatial aspects, leading to a comprehensive and reproducible approach. Full article