Search Results (6,786)

This systematic literature review explores the socio-ethical implications of Artificial Intelligence (AI) in contemporary media content generation. Drawing from 44 peer-reviewed sources, policy documents, and industry reports, the study synthesizes findings across three core domains: bias detection, storytelling transformation, and ethical governance frameworks. Through thematic coding and structured analysis, the review identifies recurring tensions between automation and authenticity, efficiency and editorial integrity, and innovation and institutional oversight. It introduces the Human–AI Co-Creation Continuum as a conceptual model for understanding hybrid narrative production and proposes practical recommendations for ethical AI adoption in journalism. The review concludes with a future research agenda emphasizing empirical studies, cross-cultural governance models, and audience perceptions of AI-generated content. This aligns with prior studies on algorithmic journalism. Full article

Museums may acquire archaeological artefacts discovered by non-specialists or amateur archaeologists, holding the potential to promote the safeguarding of cultural heritage by integrating the local community in their activities. However, this also creates an opportunity for the fraudulent sale of modern forgeries presented as archaeological artefacts, resulting in the need for a critical assessment of the artefact’s authenticity prior to acquisition by the museum. In 2019, the regional museum in Cáceres (Spain) was offered the opportunity to acquire a Phoenician-Punic head pendant, allegedly discovered in the vicinity of the city. The artefact’s authenticity was assessed by traditional approaches, including typological analysis and analysis of manufacture technique, which raised doubts about its purported age. VP-SEM-EDS analysis of the chemical composition of the different glass portions comprising the pendant was used for non-invasive determination of glassmaking recipes, enabling the identification of glass components incompatible with known Iron Age glassmaking recipes from the Mediterranean. Further comparison with historical and modern glassmaking recipes allowed for the identification of the artefact as a recent forgery made from glasses employing modern colouring and opacifying techniques. Full article

This paper presents an ethnographically informed analysis of research in Cairo and Sharm El-Sheikh (Egypt) surrounding the 2022 United Nations Framework Convention on Climate Change (UNFCCC) Conference of Parties (COP27) summit. I discuss the geopolitics and geopolitical disruptions of researching activism and activist lives in politically sensitive environments. As shown here, developing new methodological interventions plays a crucial role in understanding contextual methodological limitations, dealing with logistical challenges, and building authentic relationships with research participants. Here, I introduce counter-interviews as a methodological strategy to build trust and invest in researcher–participant relationships. This article draws on participant observation, conversations with environmental and climate activists and non-activists in Cairo prior to and after COP27 and in Sharm El-Sheikh during the second week of the summit, reflective field notes, and 20 semi-structured interviews conducted online between February and August 2023. Here, I use the term “environmental non-activism” to draw attention to the sensitivity, complexity, and fragility of political or apolitical environmental and climate action in an authoritarian context where any form of collective action is highly monitored, regulated, and sometimes criminalized by the state. The main argument of this paper is that examining interlocking power dynamics that shape and reshape the activist space in relation to the state is a requirement for understanding and researching the complexities and specificities of climate activism and non-activism in authoritarian contexts. Along with this argument, this paper invites climate education researchers to reevaluate what non-movements and non-activists in the Global South offer to their analyses of possible alternatives, socio-political change, and politics of hope (and to the broader field of activism in educational research, where commitment to disruption, refusal, and subversion play a key role. Full article

Adolescence is marked by identity formation and moral development, often accompanied by increased transgressive behaviors. While existing research highlights the interplay between moral constructs and transgression in adolescence, the role of kindness remains underexamined. This study conceptualizes kindness as a multidimensional moral construct and investigates the relationship between different stages of kindness (i.e., egocentric, social/normative, extrinsically motivated, authentic) and transgressive behaviors among adolescents, also considering the moderating role of self-importance of moral identity. The participants were 215 Italian adolescents (aged 15–19) who completed a self-report questionnaire. The results showed that egocentric and authentic kindness were positively and negatively associated with transgression, respectively. Moreover, moral identity significantly enhanced the protective role of authentic kindness. These findings suggest that the relationship between kindness and transgression varies based on the stage of kindness and the importance adolescents attribute to their moral identity. They contribute to extending the understanding of kindness during adolescence, offering implications for reducing transgressive behaviors through targeted and innovative interventions. Full article

User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal biometric system that combines fingerprint (FP) and finger vein (FV) modalities to improve accuracy and security. The system explores three fusion strategies: feature-level fusion (combining feature vectors from each modality), score-level fusion (integrating prediction scores from each modality), and a hybrid approach that leverages both feature and score information. The implementation involved five pretrained convolutional neural network (CNN) models: two unimodal (FP-only and FV-only) and three multimodal models corresponding to each fusion strategy. The models were assessed using the NUPT-FPV dataset, which consists of 33,600 images collected from 140 subjects with a dual-mode acquisition device in varied environmental conditions. The results indicate that the hybrid-level fusion with a dominant score weight (0.7 score, 0.3 feature) achieved the highest accuracy (99.79%) and the lowest equal error rate (EER = 0.0018), demonstrating superior robustness. Overall, the results demonstrate that integrating deep learning with multimodal fusion is highly effective for advancing scalable and accurate biometric authentication solutions suitable for real-world deployments. Full article

User authentication verifies the legitimacy of users and prevents service providers from offering services to unauthorized parties. The concept is widely applied in various scenarios, including everyday access control systems and IoT applications. With growing concerns about personal privacy, ensuring user anonymity has become increasingly important. In addition to privacy, user convenience is also a key factor influencing the willingness to adopt a system. To address these concerns, we propose a user authentication scheme that ensures personal privacy. The system consists of a backend server, multiple users, and multiple control units. Each user is issued or equipped with an authentication unit. An authorized user can be authenticated by a control unit, with assistance from the backend server, without revealing their identity to the control unit. The scheme is suitable for applications requiring privacy-preserving authentication. Furthermore, to enhance generality, the proposed design ensures computational efficiency and allows the authentication unit to adapt to specific application requirements. Full article

I use Shakespeare to teach acting to students. A key to my work is impression management: what Shakespeare called reputation. I view the management of reputation as a route into Shakespearean character, which I present to students as a mask attuned to sacred values. The physical basis from which the actor can discover the mask is what Hamlet calls ‘smoothness’, which I explain with an acting exercise. We discover the force of sacred values by noticing the ubiquity of keywords in the text such as honor, virtue, reason, shame and faith. By holding characters to the fire of their sacred values, I shift the actor’s attention from an individualist idea of authentic representation towards a sense of character as a battleground of mind-shaping. The resulting performance work is scaled up to a more expansive and energized degree than the actor may be used to delivering in a social media-saturated environment in which what is often prioritized is a quasi-confessional self-revelation. The revelation of an inner life then emerges through a committed exploration of antithetical relations, a strategy basic both to mask work and to Shakespeare’s poetics. The actor finds their personal connection to the material by facing the contradiction between the objective standards of behavior demanded of the character and the character’s attempt to control their status, that is, how they are seen. The final value of the performance work is that the actor learns how to manage their reputation so that they come to appear like a giant who is seen from a distance. Full article

This study empirically investigates the use of artificial intelligence (AI) tools within Hungarian religious communities, with a focus on Catholic respondents, to assess their awareness, application, and acceptance of AI in religious contexts. By religious communities, we do not mean monastic or priestly communities, but rather communities of lay religious people. Conducted between 10 February and 11 March 2025, the questionnaire-based research (N = 133) employs Campbell’s Religious Social Shaping of Technology (RSST) framework to analyze attitudes toward AI across 15 religious functions. Six hypotheses explore gender differences, religiosity types (church-based vs. self-defined), and the acceptability, authenticity, and ethicality of AI applications. Findings reveal high acceptance for administrative tasks (e.g., email list updates: 64.7%) and technical functions (e.g., live translation: 65.4%), but low acceptance for spiritual roles (e.g., spiritual leadership: 12.8%). Self-defined religious individuals are significantly more accepting, perceiving AI as more authentic and ethical compared to those adhering to church teachings. No significant gender differences were found. The study contributes to digital religion studies, highlighting the influence of religiosity on AI adoption, though its non-representative sample limits generalizability. Full article

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. Therefore, identity revocation technology in the authentication is an important way to secure CAVs and other 6G scenario applications. This paper proposes an efficient credential revocation scheme with a four-layer architecture. First, a rapid pre-filtration layer is constructed based on the cuckoo filter, responsible for the initial screening of credentials. Secondly, a directed routing layer and the precision judgement layer are designed based on the consistency hash and the dynamic RSA accumulator. By proposing the dynamic expansion of the RSA accumulator and load-balancing algorithm, a smaller and more stable revocation delay can be achieved when many users and terminal devices access 6G. Finally, a trusted storage layer is built based on the blockchain, and the key revocation parameters are uploaded to the blockchain to achieve a tamper-proof revocation mechanism and trusted data traceability. Based on this architecture, this paper also proposes a detailed identity credential revocation and verification process. Compared to existing solutions, this paper’s solution has a combined average improvement of 59.14% in the performance of the latency of the cancellation of the inspection, and the system has excellent load balancing, with a standard deviation of only 11.62, and the maximum deviation is controlled within the range of ±4%. Full article

This study explores the emergent dynamics of knowledge sovereignty within organisations following data breach incidents. Using qualitative analysis based on Benoit’s image restoration theory, this study shows that employees do more than relay official messages—they actively shape information governance after a cyberattack. Employees adapt Benoit’s response strategies (denial, evasion of responsibility, reducing offensiveness, corrective action, and mortification) based on how authentic they perceive the organisation’s response, their identification with the company, and their sense of fairness in crisis management. This investigation substantively extends extant crisis communication theory by showing how knowledge sovereignty is shaped through negotiation, as employees manage their dual role as breach victims and organisational representatives. The findings suggest that employees are key actors in post-breach information governance, and that their authentic engagement is critical to organisational recovery after cybersecurity incidents. Full article

In December 2023, infections of western equine encephalitis virus (WEEV) within Argentina were reported to the World Health Organization (WHO). By April 2024, more than 250 human infections, 12 of which were fatal, and 2500 equine infections were identified in South America. Laboratory diagnosis and surveillance in affected countries were hindered by a lack of facilities equipped with BSL-3 laboratories, as confirmatory serodiagnosis for WEEV requires live virus in the plaque reduction neutralization test (PRNT). To expand serodiagnosis for WEEV in the Americas, we developed a virus chimera composed of vesicular stomatitis virus (VSV) engineered to display the E2-E1 glycoproteins of WEEV (VSV/WEEV) in place of the VSV glycoprotein (G). PRNT₉₀ and IC₉₀ values of parental WEEV and VSV/WEEV were analogous using sera collected from mice, horses, and chickens. VSV/WEEV rapidly formed plaques with clear borders and reduced the assay readout time by approximately 8 h compared to the parental virus. Overall, we demonstrate that chimeric VSV/WEEV is a suitable surrogate for WEEV in a diagnostic PRNT. Use of chimeric VSV/WEEV in place of authentic WEEV will dramatically expand testing capacity by enabling PRNTs to be performed at BSL-2 containment, while simultaneously decreasing the health risk to testing personnel. Full article

Geographical origin authentication of agrifood products is essential for ensuring their quality, preventing fraud, and maintaining consumers’ trust. In this study, we used proton nuclear magnetic resonance (¹H NMR) and excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the geographical origin characterization of olive drupes and leaves from different Tuscany subregions, where olive oil production is relevant. Single-block approaches were implemented for individual datasets, using principal component analysis (PCA) for data visualization and Soft Independent Modeling of Class Analogy (SIMCA) for sample classification. ¹H NMR spectroscopy provided detailed metabolomic profiles, identifying key compounds such as polyphenols and organic acids that contribute to geographical differentiation. EEM fluorescence spectroscopy, in combination with Parallel Factor Analysis (PARAFAC), revealed distinctive fluorescence signatures associated with polyphenolic content. A mid-level data fusion strategy, integrating the common dimensions (ComDim) method, was explored to improve the models’ performance. The results demonstrated that both spectroscopic techniques independently provided valuable insights in terms of geographical characterization, while data fusion further improved the model performances, particularly for olive drupes. Notably, this study represents the first attempt to apply EEM fluorescence for the geographical classification of olive drupes and leaves, highlighting its potential as a complementary tool in geographic origin authentication. The integration of advanced spectroscopic and chemometric methods offers a reliable approach for the differentiation of samples from closely related areas at a subregional level. Full article

Tocopherols, major lipid-soluble components of vitamin E, are essential natural products with significant nutritional and pharmacological value. Their structural diversity and uneven distribution across vegetable oils require accurate analytical strategies for compositional profiling, quality control, and authenticity verification, amid concerns over food fraud and regulatory demands. Analytical challenges, such as matrix effects in complex oils and the cost trade-offs of green extraction methods, complicate these processes. This review examines recent advances in tocopherol analysis, focusing on extraction and detection techniques. Green methods like supercritical fluid extraction and deep eutectic solvents offer selectivity and sustainability, though they are costlier than traditional approaches. On the analytical side, hyphenated techniques such as supercritical fluid chromatography-mass spectrometry (SFC-MS) achieve detection limits as low as 0.05 ng/mL, improving sensitivity in complex matrices. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides robust analysis, while spectroscopic and electrochemical sensors offer rapid, cost-effective alternatives for high-throughput screening. The integration of chemometric tools and miniaturized systems supports scalable workflows. Looking ahead, the incorporation of Artificial Intelligence (AI) in oil authentication has the potential to enhance the accuracy and efficiency of future analyses. These innovations could improve our understanding of tocopherol compositions in vegetable oils, supporting more reliable assessments of nutritional value and product authenticity. Full article

Globalization and market saturation have led Portuguese textile companies to seek international markets not only for growth but also to contribute to their country’s international image. This study aims to explore how the internationalization of the Portuguese textile sector into the Chinese market contributes to Portugal’s destination image and identify the critical success factors in this process. The research follows an inductive, qualitative methodology based on semi-structured interviews with two groups of companies: those already operating in China (n = 5) and those preparing to enter the market (n = 5). The interviews were thematically analyzed to extract key patterns and insights. The findings reveal that successful companies operate in the luxury segment, rely on prior international experience, and often use local intermediaries. Firms planning to internationalize highlight quality differentiation, brand authenticity, and innovation as strategic advantages. These insights support the role of niche positioning and cultural adaptation in building both commercial success and a refined international image of Portugal. This study contributes to the literature by linking internationalization and destination branding through industry-specific case evidence and offers practical implications for managers targeting emerging markets like China. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article