Search Results (1,131)

Cultured meat holds the potential to reduce environmental impacts and offer ethical advantages while replicating the nutritional, taste, and texture attributes of conventional meat. To date, most research on consumer acceptance of meat has focused on European and North American markets. In contrast, Muslim-majority countries remain underexplored, particularly regarding the compatibility of cultured meat with Islamic dietary laws. These societies are experiencing rising meat consumption, and countries such as Saudi Arabia and Malaysia rely heavily on meat imports. This narrative perspective article aims to systematically examine how specific stages of cultured meat production align with, or challenge, Islamic dietary (halal) principles. To this end, we adopt a stage-based analytical approach, mapping key technological steps in cultured meat production onto core requirements of Islamic jurisprudence. To this end, we critically and comprehensively examine the intersection between cultured meat production methods and the Islamic concept of halal, which extends beyond ingredient permissibility to encompass ethical, spiritual, and hygienic dimensions of food production. Key challenges to halal certification include the origin and status of starter cells, whether donor animals were slaughtered according to Islamic law, the permissibility of biopsied tissue, and the use of fetal bovine serum in growth media. The analysis indicates that while halal-compliant cultured meat is scientifically feasible, its adoption remains constrained by unresolved religious interpretations, regulatory fragmentation, and limited availability of halal-certified inputs. We emphasize the need for interdisciplinary collaboration among Islamic scholars, food scientists, certification bodies, and policymakers. From a policy perspective, harmonized halal standards, targeted investment in serum-free and animal-free culture media, and early regulatory engagement with Islamic authorities are essential to facilitate responsible market entry. Therefore, we suggest a multi-level governance and stage-gated halal decision framework for cultured meat. Proactive regulation and open dialogue with religious leaders are vital to ethically introduce cultured meat into Muslim markets, aligning innovation with Islamic values while supporting national sustainability and food security goals. Full article

In this study, we consider an extended form of the Kadomtsev–Petviashvili–Boussinesq equation motivated by wave propagation phenomena in dissipative media. The primary aim of this work is to construct exact analytical solutions and clarify the types of nonlinear wave structure admitted by the considered model. For this purpose, the Riccati equation expansion method is applied for the first time within this framework. This method allows us to obtain several distinct families of solitary wave solutions whose qualitative behaviors and physical characteristics are illustrated through graphical representations. In addition, modulation instability analysis is carried out to assess the stability of continuous wave solutions and further elucidate the underlying nonlinear dynamics of the system. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

Existing studies on greenwashing have primarily focused on post-incident supervision, with limited attention given to proactive mechanisms. This study aims to develop an early warning evaluation model for greenwashing behavior in building materials enterprises exposed to negative public opinion. The main findings are as follows: (1) Drawing on actor network theory, gray system theory, the analytic network process, and gray fuzzy comprehensive evaluation, this study constructs an early warning evaluation model for greenwashing behavior in building materials enterprises. This model comprises 5 first-level dimensions and 20 s-level indicators, integrating key stakeholders (i.e., government, negative public opinion, media, the public, and enterprise) and is validated through case analysis. (2) Government dimension: Environmental regulation intensity emerges as the most critical indicator. (3) Negative public opinion dimension: Attention is the most critical indicator. (4) Media dimension: Media visibility ranks as the most critical indicator. (5) Public dimension: Public sentiment is the most influential indicator. (6) Enterprise dimension: The environmental performance level is the most critical indicator. This study offers both theoretical and practical foundations for the early warning, monitoring, and governance of enterprise greenwashing, contributing to the advancement of sustainable development and transparent environmental communication in the building materials industry. Full article

Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems, especially in multilingual contexts. This paper introduces a reliability-aware transformer-based framework for analyzing sentiment in agricultural market intelligence across multiple languages. The framework leverages weakly supervised multilingual transformers to extract sentiment signals from large-scale unlabeled Thai and English texts about major agricultural commodities found online. To enhance robustness under weak supervision, the framework incorporates reliability-aware mechanisms, including confidence-based pseudo-label filtering, cross-source consistency refinement, and expert-guided calibration to reduce noise and account for bias between different data sources. Sentiment predictions are further aligned with market intelligence objectives through reliability-weighted aggregation, yielding interpretable sentiment indices that enable cross-lingual and cross-source comparability. We tested the framework extensively using a multilingual agricultural corpus derived from social media and news coverage of agriculture. The results show consistent improvements over both classical machine learning approaches and standard multilingual transformer baselines. Additional ablation studies and sensitivity analyses confirmed that reliability-aware mechanisms, particularly confidence thresholding, play a crucial role in getting the right balance between label quality and data coverage. Overall, the results indicate that reliability-aware multilingual sentiment analytics provide robust and actionable insights for agricultural market monitoring and policy analysis. Full article

The integration of generative artificial intelligence (GAI) into digital learning environments is a profound socio-technical transformation. While GAI promises enhanced accessibility and efficiency, it simultaneously obscures the human creativity and intellectual labour that underpins digital knowledge production. This opacity limits creators’ visibility into how their work is used, evaluated, and monetised. This review application work investigates how several leading large language models, including ChatGPT (GPT-4o), Gemini (1.5 Flash), and DeepSeek (V3), interact with a creative platform hosting over 300 original essays, poems, and artworks from various human creatives. Our review reveals that despite clear evidence of models engaging with original materials, standard platform analytics of the average creative record no attribution, referrals, or traceable interaction from their end, rendering creators’ labour invisible. This compels critical examination of knowledge provenance and power within AI-mediated education. To address this, we propose a socio-technical framework, Chujoyi-TraceNet, not as a technical fix, but a mechanism to re-centre ethics, justice, and recognition in digital governance. By integrating real-time tracking, blockchain-enabled licensing, and metadata watermarking, Chujoyi-TraceNet operationalises the principles of equitable attribution. This study argues for a re-imagining of digital ecosystems in education, one that links the technical act of attribution to broader debates on digital labour, platform ethics, and the pursuit of social justice, thereby contributing to more democratic and accountable learning media in the era of Industry 4.0 and 5.0. Full article

We develop a theoretical description of the electrophoretic migration of a weakly charged oil drop dispersed in a dilute polymer gel carrying fixed charges and saturated with an aqueous electrolyte solution. In contrast to neutral gels, a charged polymer network generates electroosmotic flow under an applied electric field, which couples with the electrohydrodynamic motion of the drop. The observed electrophoretic velocity therefore reflects the combined effects of drop-induced flow and gel-driven electroosmosis. On the basis of the Baygents–Saville theory, the drop surface charge is assumed to originate from specific ion adsorption at the oil–water interface, while no mobile ions are present inside the drop. Working within the Brinkman–Debye–Bueche porous-medium model for the gel and employing a linearized treatment valid for low zeta potential, we obtain a simple analytical expression for the electrophoretic mobility. The formulation consistently incorporates Marangoni stresses arising from spatial variations in interfacial tension, and hydrodynamic slip at the oil–water interface, which can be significant for hydrophobic drops in aqueous media. The resulting mobility expression explicitly separates the contribution associated with the intrinsic electrohydrodynamic response of the drop from that due to electroosmosis of the charged gel matrix. This analytical form enables experimental mobility data to be used not only to estimate the zeta potential of the drop but also to evaluate the electroosmotic mobility of the polymer gel medium. The present theory thus provides a physically transparent and experimentally useful framework for characterizing electrokinetic transport in charged soft porous media. Full article

This article examines the Americanization of television news in post-communist media systems through an in-depth case study of bTV, Bulgaria’s first national commercial television broadcaster, launched by News Corporation in 2000. Drawing on original in-depth qualitative interviews with founding executives, journalists, regulators, and consultants, alongside archival materials and documentary analysis, the study traces how U.S. journalistic norms were introduced, negotiated, and ultimately hybridized within a fragile post-socialist media environment. Building on Gabriel Tarde’s theory of imitation, the article proposes a three-stage analytical model—transmission, transnationalization, and appropriation—to capture the dynamics of media transformation beyond simple adoption or rejection. The findings show that Americanization initially operated as a professionalizing force, reshaping visual storytelling, newsroom routines, and narrative structures, while also functioning as a symbolic and structural shield against overt political interference. Foreign ownership, particularly American ownership, was widely perceived by media actors as a buffer separating newsrooms from local power networks and enabling a degree of editorial autonomy. At the appropriation stage, however, the analysis reveals a more ambivalent outcome. While American formats and aesthetics were rapidly internalized at the surface level, deeper journalistic identities and democratic functions (most notably the Fourth Estate ideal) were only partially and unevenly appropriated. The result was a hybrid media model characterized by format mixing, depoliticization, and selective adaptation to local cultural and institutional legacies. The article conceptualizes this outcome as a Copycat Fourth Estate: a media system that appears American in form yet remains shaped by post-communist legacies of control, accommodation, and limited civic engagement. By offering a historically grounded, outlet-level analysis, the study contributes to debates on media Americanization, hybridization, and media capture, and advances understanding of how imported journalistic models are reshaped in transitional democracies. Full article

Water pollution is one of the most critical societal and environmental challenges and remains a persisting problem worldwide. The origin of this pollution is diverse, while organic matter occupies a significant portion, originating from different sources. This creates major environmental and health risks, requiring reliable and sensitive analytical tools for effective monitoring. The permanganate index stands as a conventional assessment method for organic pollution, but it demonstrates compound non-specificity toward compounds and limited sensitivity to various contaminant structures. This research introduces cyclic voltammetry as a standalone electrochemical method that provides sensitive detection and characterization of organic oxidizing compounds. Six organic compounds, including gallic acid, phenol, oxalic acid, ascorbic acid, salicylic acid and p-benzoquinone, were used as model compounds and studied in aqueous media. These compounds were analyzed individually, in single-compound mode, to characterize their redox behavior and to identify the voltammetric peaks. Subsequently, a multi-compound analysis was studied to check for the validity of the concept in a more complex matrix. Notably, a strong linear correlation was observed between the measured charge and the theoretical permanganate index, highlighting the quantitative reliability of the electrochemical method. Comparing the obtained results with the permanganate index method confirmed the superiority of cyclic voltammetry in terms of response time and detection capability. The outcomes demonstrate that cyclic voltammetry functions as a robust alternative to the classical chemical oxidation method for environmental water assessment. Full article

Social network content is increasingly used as an auxiliary evidence stream for financial monitoring, risk assessment, and short-horizon decision support, yet many reported gains are hard to interpret because observability, timing, and attribution are handled inconsistently across studies. This review critically synthesizes the end-to-end pipeline that transforms social posts, interaction traces, linked artifacts, and related signals into decision-facing indicators, emphasizing evidence provenance, sampling bias, conditioning (bot/spam filtering, entity linking, timestamp alignment), and the modeling blocks typically used (text, temporal, relational, and fusion components) under deployment constraints. Across sentiment, relational, and multimodal or cross-platform signals, the analysis finds that apparent improvements often depend more on alignment discipline and conservative attribution than on architectural novelty, and that performance can be inflated by attention confounds, temporal leakage, and visibility effects. Relational indicators are most defensible for monitoring coordination and propagation patterns, while multimodal gains require clear ablations and realistic missing-modality tests. To support decision readiness, the paper consolidates assurance requirements covering manipulation, degraded observability, calibration and traceability, and provides compact reporting checklists and failure-mode mitigations. Overall, the review supports bounded claims and argues for time-aware evaluation and auditable pipelines as prerequisites for operational use. Full article

Media-based cultural diversity education is approached here as an analytical synthesis that brings together established research traditions in media and communication studies, including mediatization, representation, and framing. It refers to the process through which media are understood to function as informal educational environments that shape how audiences learn about and interpret cultural differences. In contemporary mediatized societies, media institutions, including television and digital platforms, are understood to shape public understandings of diversity through the selection, framing, and visual representation of minority groups. Television is widely regarded as a particularly influential medium because of its wide reach and its institutional role in producing authoritative narratives about social reality. Through news reporting, documentaries, and other factual programming, television has been shown to circulate meanings about cultural diversity and provide audiences with interpretive frameworks through which minority groups are publicly understood. These communicative practices have been shown to influence how audiences perceive cultural difference, interpret social issues, and negotiate questions of belonging within society. By organizing narratives, frames, and visual repertoires through which cultural groups are portrayed, television has been shown to contribute to the formation of shared social knowledge about diversity and about relationships between majority and minority communities. In this sense, television can be understood not only as a channel of information but also as a cultural institution that shapes symbolic boundaries between social groups and influences perceptions of inclusion and exclusion. As an illustrative context, this entry also refers to representations of Roma communities in Central European media environments, where antigypsyism may be understood as a mediated cultural process embedded in everyday media communication. Full article

Industrial heritage in resource-depleted mining towns faces the dual challenge of physical decay and social severance. To achieve sustainable urban revitalization, adaptive reuse strategies must align with local collective memory and emerging experiential consumption trends. Adopting a Scene Theory perspective, this study constructs a multi-level analytical framework using Meitanba Town (Hunan, China) and its power plant as a case study. A mixed-methods approach was employed, combining semantic network analysis of 1582 online user comments with 61 offline questionnaires distributed to local residents to quantitatively diagnose current scene elements, functions, and features. The quantitative results reveal a significant imbalance: while “Functional Media” achieved the highest comprehensive score (10.0) due to strong historical recognition, “Diverse Groups” scored the lowest (3.4), indicating a lack of social inclusivity. Specifically, residents expressed the highest demand for sports facilities (31.2%) and cultural spaces (23.7%), identifying the main workshop (26.4%) and chimney as core carriers of industrial identity. Responding to these findings, the paper proposes three targeted strategies: (1) Activate: creating open-access recreation scenes to satisfy urgent sports demands; (2) Link: constructing immersive cultural scenes to narrate the “coal–electricity–life” history; and (3) Enhance: developing industry-powered commercial scenes to avoid homogenization. This study enriches the localized application of Scene Theory and provides a data-driven, context-adjustable analytical and strategic model that can inform the sustainable renewal of mining towns globally, with its specific implementation requiring adaptation to local social, economic, and cultural characteristics. Full article

Abusive news refers to digital content designed to maximize clicks and advertising revenue through sensational headlines, repetitive postings, or emotionally charged language, rather than upholding journalistic integrity. Despite growing concerns about its impact on media credibility and public trust, existing detection approaches lack systematic categorization and type-specific methodologies. This study addresses this gap by proposing a six-type typology of abusive news—content recycling, keyword insertion, title–body inconsistency, commercial promotion, emotionally stimulating headline, and automatically generated types—based on five analytical dimensions: content structure, authenticity, algorithmic manipulability, sensationalism, and information-ecosystem impact. We developed type-specific detection pipelines combining BERT-based embeddings, TF-IDF features, and rule-based indicators and evaluated them using a large-scale Korean clickbait corpus. Results demonstrate that BERT achieves higher F1-scores (0.89) for automatically generated content, while TF-IDF with SVM provides more stable precision (0.60) for emotionally charged articles under class imbalance. Cross-domain experiments confirm that models trained on diverse, balanced topic sets generalize better than volume-focused models, with diversity improving F1-scores by up to 0.07. BERT models show higher false positive rates on repetitive legitimate content compared to TF-IDF approaches, highlighting the importance of type-adaptive architectures and diversity-aware data design in abusive news detection systems. Full article

Fractional differential equations provide a flexible framework for describing evolutionary processes in complex media, where nonlocality and memory effects play central roles, and classical integer-order models are frequently inadequate to capture these behaviors. In this work, we revisit the time-fractional Harry Dym (HD) evolution equation in the Caputo sense and construct high-precision analytical approximations using the recently developed Tantawy technique (TT). The method generates a rapidly convergent fractional-power series in time without resorting to perturbative assumptions, auxiliary decomposition polynomials, linearization procedures, or integral transforms, and it remains computationally economical even at high approximation orders. Closed, compact expressions are derived up to the fifth-order approximation and can be systematically extended, yielding excellent agreement with the known exact solution of the classical/integer HD model and with approximations obtained via the new iterative method. A detailed error analysis is carried out by computing absolute and maximum residual errors over the entire computational domain, demonstrating the accuracy, stability, and robustness of the TT for the HD-type fractional nonlinear evolution equation. From a physical perspective, the proposed framework offers a reliable tool for modeling nonlinear wave structures in dispersive media with significant memory and, more generally, for treating a broad class of fractional nonlinear wave equations arising in physics and engineering. Full article

This study presents an analytical investigation of the dynamic behavior of concrete beams reinforced with different types of nano-clay (NC) particles and resting on a Winkler–Pasternak elastic foundation. The equivalent elastic properties of the nanocomposite were determined using an Eshelby-based homogenization model. An improved quasi-three-dimensional beam theory was applied to formulate the governing equations of motion, which were subsequently then analytically solved using Navier’s method. The analysis shows that NC reinforcement systematically elevates the natural frequencies of the beam, with the magnitude of improvement varying by particle type and concentration. Increasing the NC volume fraction to 30% leads to a significant rise in the fundamental frequency, reaching about 30% for hectorite (SHca-1) compared with the unreinforced beam, whereas montmorillonite (SWy-1) produces a more moderate increase of approximately 13%. This reinforcing effect remains consistent across different span-to-depth ratios, indicating that the influence of nano-clay content on the dynamic response is largely independent of beam slenderness. Furthermore, increasing the Winkler foundation stiffness results in an almost linear rise in frequency of approximately 18–22%, whereas the Pasternak shear parameter produces a stronger effect, reaching around 25% enhancement depending on the reinforcement type. These results indicate that incorporating nano-clay platelets can be an effective strategy for enhancing the vibrational stiffness of concrete beams and improving their dynamic performance when interacting with supporting soil media. Full article