Search Results (8)

The tourism industry is witnessing an increase in innovative AI-generated content (AIGC). AI-generated images are a key visual form of AIGC, and this study focuses on a specific subset of them: AI-generated images that realistically depict destination scenery, which we define as AI-generated photos (AIGP). Based on construal level theory, this study explored the visual cues of AIGP. Two empirical studies were conducted to examine the influence of scene presentation within AIGP on tourists’ travel intention. The findings revealed that panoramic series in AIGP, as opposed to close-up series, significantly enhanced travel intention. The results further suggested an indirect association through destination images and a moderating role of AIGP source. Specifically, the effects of scene presentation were found to be diminished when an AIGP was sourced from user-generated content. This study makes several notable contributions. First, it expands the application of AIGP within the tourism sector. Second, it enriches the literature on visual cues and destination images by delineating the process through which AIGP scene presentation influences travel intention. Third, the research clarifies the moderating role of the AIGP source, expanding the boundary of AIGP effects. These insights add depth to the dual fields of social media and destination marketing, highlighting the contextual variability in the efficacy of AIGC-driven marketing strategies. Full article

Underground Gas Storage (UGS) is transitioning from traditional fossil fuel peak-shaving facilities into comprehensive hubs for Terawatt-hour-scale Terawatt-hour (TWh) scale renewable energy storage. The unique physicochemical properties of diverse fluids, such as the negative Joule–Thomson coefficient of hydrogen (−0.03 K/bar), present complex engineering adaptability challenges. Since existing studies primarily focus on single mechanisms or specific geological types, this review integrates a unified engineering framework to evaluate the repurposing potential and retrofitting requirements of existing oil and gas assets. By compiling a property benchmarking matrix for methane, carbon dioxide, and hydrogen, the storage adaptability of various geological formations is summarized. Salt caverns exhibit strong adaptability to highly diffusive and reactive fluids due to their high salinity (exceeding 150 g/L) and mechanical stability, whereas porous media offer massive capacity (more than 10 times) but require overcoming severe biogeochemical obstacles. Based on thermo–hydro–mechanical–chemical–biological (THMCB) coupling mechanisms, an integrity evaluation system for artificial wellbore and natural geological barriers is systematically reviewed. Critical risks, including fatigue failure under high-frequency cyclic loading, material degradation, gas leakage, and indirect Global Warming Potential (GWP), are elucidated. A future evolution route integrating physical, digital, and policy dimensions is outlined. This roadmap emphasizes Hydrogen-Enriched Compressed Natural Gas (HCNG)synergistic storage, dynamic risk control utilizing digital twins and Artificial Intelligence (AI), and standardized Life Cycle Assessment mechanisms (LCA), providing a scientific basis for the sustainable transition of UGS facilities. Full article

The rapid development of artificial intelligence (AI) is reshaping innovation models and enterprises’ competitive advantages. However, existing research has not systematically examined whether AI adoption enhances enterprise innovation or identified its influencing factors. Using panel data from Chinese A-share listed companies between 2012 and 2023, this study constructed an index measuring the extent of AI adoption based on annual report texts and analyzed its impact on enterprise innovation efficiency and underlying mechanisms. The results indicate that AI adoption significantly improves innovation efficiency. Further analysis shows that institutional ownership, executives’ digital background (EDB), and media attention positively moderate this effect. This study provides new evidence for the governance boundaries of AI-driven innovation, enriches enterprise innovation theory in the context of digital transformation, and offers managerial insights for enterprises seeking to realize the value of digital technologies. Full article

The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%. Full article

Salmonella serovar identification typically requires multiple enrichment steps using selective media, consuming considerable time and resources. This study presents a rapid, culture-independent method leveraging artificial intelligence (AI) to classify Salmonella serovars from rich hyperspectral microscopy data. Five serovars (Enteritidis, Infantis, Kentucky, Johannesburg, 4,[5],12:i:-) were analyzed from samples prepared using only sterilized de-ionized water. Hyperspectral data cubes were collected to generate single-cell spectra and RGB composite images representing the full microscopy field. Data analysis involved two parallel branches followed by multimodal fusion. The spectral branch compared manual feature selection with data-driven feature extraction via principal component analysis (PCA), followed by classification using conventional machine learning models (i.e., k-nearest neighbors, support vector machine, random forest, and multilayer perceptron). The image branch employed a convolutional neural network (CNN) to extract spatial features directly from images without predefined morphological descriptors. Using PCA-derived spectral features, the highest performing machine learning model achieved 81.1% accuracy, outperforming manual feature selection. CNN-based classification using image features alone yielded lower accuracy (57.3%) in this serovar-level discrimination. In contrast, a multimodal fusion model combining spectral and image features improved accuracy to 82.4% on the unseen test set while reducing overfitting on the train set. This study demonstrates that AI-enabled hyperspectral microscopy with multimodal fusion can streamline Salmonella serovar identification workflows. Full article

With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the application of natural language processing (NLP) and artificial intelligence (AI). This study applies a novel hybrid-structure Hybrid Transformer–Enriched Attention with Multi-Domain Dynamic Attention Network (Hyb-KAN), which combines a transformer-based architecture, an attention mechanism, and BiLSTM recurrent neural networks. In this study, a multi-class classification method is used to identify comments containing cyberbullying features. For better verification, we compared the proposed method with baseline methods. The Hyb-KAN model demonstrated high results on the multi-class classification dataset, achieving an accuracy of 95.25%. The synergy of BiLSTM, Transformer, MD-DAN, and KAN components provides flexibility and accuracy of text analysis. The study used explainable visualization techniques, including SHAP and LIME, to analyze the interpretability of the Hyb-KAN model, providing a deeper understanding of the decision-making mechanisms. In the final stage of the study, the results were compared with current research data to confirm their relevance to current trends. Full article

Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called “Human Nature Test”. In this context, “human nature” refers to emotion and consciousness, while “test” involves a critical analysis of AI technology and an exploration of the differences between humanity and technicality. Additionally, through experimental research and literature analysis, we found that the integration of electroencephalogram (EEG) data with AI systems is becoming a significant trend. The experiment involved 20 participants, with two conditions: C1 (using EEG data) and C2 (without EEG data). The results indicated a significant increase in immersion under the C1 condition, along with a more positive emotional experience. We summarized three design directions: enhancing immersion, creating emotional experiences, and expressing philosophical concepts. Based on these findings, there is potential for further developing EEG data as a medium to enrich interactive experiences, offering new insights into the fusion of technology and human emotion. Full article

In recent years, with the growing accessibility of abundant contextual emotion information, which is benefited by the numerous georeferenced user-generated content and the maturity of artificial intelligence (AI)-based emotional computing technics, the emotion layer of human–environment relationship is proposed for enriching traditional methods of various related disciplines such as urban planning. This paper proposes the geographic information system (GIS)-based emotional computing concept, which is a novel framework for applying GIS methods to collective human emotion. The methodology presented in this paper consists of three key steps: (1) collecting georeferenced data containing emotion and environment information such as social media and official sites, (2) detecting emotions using AI-based emotional computing technics such as natural language processing (NLP) and computer vision (CV), and (3) visualizing and analyzing the spatiotemporal patterns with GIS tools. This methodology is a great synergy of multidisciplinary cutting-edge techniques, such as GIScience, sociology, and computer science. Moreover, it can effectively and deeply explore the connection between people and their surroundings with the help of GIS methods. Generally, the framework provides a standard workflow to calculate and analyze the new information layer for researchers, in which a measured human-centric perspective onto the environment is possible. Full article