Search Results (230)

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

As multimedia technologies evolve, digital images have become increasingly prevalent across various fields, highlighting an urgent demand for robust image privacy and security mechanisms. However, existing image encryption algorithms (IEAs) still face limitations in balancing strong security, real-time performance, and computational efficiency. Therefore, we proposes a new IEA that integrates an improved chaotic map (Tent map), an improved Zigzag transform, and dynamic DNA coding. Firstly, a pseudo-wavelet transform (PWT) is applied to plain images to produce four sub-images $I_1$, $I_2$, $I_3$, and $I_4$. Secondly, the improved Zigzag transform and its three variants are used to rearrange the sub-image $I_1$, and then the scrambled sub-image is diffused using XOR operation. Thirdly, an inverse pseudo-wavelet transform (IPWT) is employed on the four sub-images to reconstruct the image, and then the reconstructed image is encoded into a DNA sequence utilizing dynamic DNA encoding. Finally, the DNA sequence is scrambled and diffused employing DNA-level index scrambling and dynamic DNA operations. The experimental results and performance evaluations, including chaotic performance evaluation and comprehensive security analysis, demonstrate that our IEA achieves high key sensitivity, low correlation, excellent entropy, and strong resistance to common attacks. This highlights its potential for deployment in real-time, high-security image cryptosystems, especially in fields such as medical image security and social media privacy. Full article

As digital transformation progresses, schools are increasingly confronted with psychosocial challenges such as technostress, digital overload, and unequal participation in digital (learning) environments. This article investigates the conceptual relevance of digital well-being for school development, particularly in relation to social inequality. Despite growing attention, the term remains theoretically underdefined in educational research—a gap addressed through a theory-driven review. Drawing on a systematic search, 25 key studies were analyzed for their conceptual understanding and refinement of digital well-being, with a focus on educational relevance. Findings suggest that digital well-being constitutes a multidimensional state shaped by individual, media-related, and socio-structural factors. It emerges when individuals are able to successfully manage the demands of digital environments and is closely linked to digital inequality—particularly in terms of access, usage practices, and the resulting opportunities for participation and health promotion. Since the institutional role of schools has thus far received limited attention, this article shifts the focus toward schools as key arenas for negotiating digital norms and practices and calls for an equity-sensitive and health-conscious perspective on school development in the context of digitalization. In doing so, digital well-being is repositioned as a pedagogical cross-cutting issue that requires coordinated efforts across all levels of the education system, highlighting that equitable digital transformation in schools depends on a critical reflection of power asymmetries within society and educational institutions. The article concludes by advocating for the systematic integration of digital well-being into school development processes as a way to support inclusive digital participation and to foster a health-oriented digital school culture. Full article

Single or combined heat pump technologies are generally used to dry fruits and vegetables, with combined heat pump technologies offering superior performance. This review summarizes the applications of single and combined heat pump drying technologies for fruits and vegetables in China and globally, discusses their current advantages and disadvantages, and outlines future development directions for heat pump-based drying methods. Future research should focus on improving combined heat pump technologies and enhancing the performance of single heat pump drying systems to enhance the effectiveness and feasibility of these technologies for drying fruits and vegetables. Improved technologies would also help meet the increasing demand for high-quality food and social development. Moreover, changes in the mechanisms of key indicators, such as mechanical and thermodynamic properties, should be continuously monitored while drying various fruits and vegetables. Future research into combined heat pump technologies should focus on determining the conversion methods between pairs of drying technologies and identifying the most effective drying technology combinations. Future research into single heat pump technologies should focus on improving the performance levels of core components, such as compressors and drying media. Full article

Background: High-voltage electrical injuries (HVEIs) represent a complex and life-threatening entity, frequently involving multi-organ damage. While traditionally linked to occupational hazards, train surfing—riding on moving trains—and train climbing—scaling stationary carriages—have emerged as increasingly common causes among adolescents. Popularized via social media, these behaviors expose individuals to the invisible danger of electric arcs from 15,000-volt railway lines, often resulting in extensive burns, cardiac complications, and severe trauma. This study presents a 30-year retrospective analysis comparing cardiac biomarkers and clinical outcomes in train-surfing injuries versus work-related HVEIs. Methods: All patients with confirmed high-voltage injury (≥1000 volts) admitted to a Level 1 burn center between 1994 and 2024 were retrospectively analyzed. Exclusion criteria comprised low-voltage trauma, suicide, incomplete records, and external treatment. Clinical and laboratory parameters—including total body surface area (TBSA), Abbreviated Burn Severity Index (ABSI), electrocardiogram (ECG) findings, intensive care unit (ICU) and hospital stay, mortality, and cardiac biomarkers (creatine kinase [CK], CK-MB, lactate dehydrogenase [LDH], aspartate transaminase [AST], troponin, and myoglobin)—were compared between the two cohorts. Results: Of 81 patients, 24 sustained train-surfing injuries and 57 were injured in occupational settings. Train surfers were significantly younger (mean 16.7 vs. 35.2 years, p = 0.008), presented with greater TBSA (49.9% vs. 17.9%, p = 0.008), higher ABSI scores (7.3 vs. 5.1, p = 0.008), longer ICU stays (53 vs. 17 days, p = 0.008), and higher mortality (20.8% vs. 3.5%). ECG abnormalities were observed in 51% of all cases, without significant group differences. However, all cardiac biomarkers were significantly elevated in train-surfing injuries at both 72 h and 10 days post-injury (p < 0.05), suggesting more pronounced cardiac and muscular damage. Conclusions: Train-surfing-related high-voltage injuries are associated with markedly more severe systemic and cardiac complications than occupational HVEIs. The significant biomarker elevation and critical care demands highlight the urgent need for targeted prevention, public awareness, and early cardiac monitoring in this high-risk adolescent population. Full article

The widespread rise of misinformation across digital platforms has increased the demand for accurate and efficient Fake News Detection (FND) systems. This study introduces an enhanced transformer-based architecture for FND, developed through comprehensive ablation studies and empirical evaluations on multiple benchmark datasets. The proposed model combines improved multi-head attention, dynamic positional encoding, and a lightweight classification head to effectively capture nuanced linguistic patterns, while maintaining computational efficiency. To ensure robust training, techniques such as label smoothing, learning rate warm-up, and reproducibility protocols were incorporated. The model demonstrates strong generalization across three diverse datasets, such as FakeNewsNet, ISOT, and LIAR, achieving an average accuracy of 79.85%. Specifically, it attains 80% accuracy on FakeNewsNet, 100% on ISOT, and 59.56% on LIAR. With just 3.1 to 4.3 million parameters, the model achieves an 85% reduction in size compared to full-sized BERT architectures. These results highlight the model’s effectiveness in balancing high accuracy with resource efficiency, making it suitable for real-world applications such as social media monitoring and automated fact-checking. Future work will explore multilingual extensions, cross-domain generalization, and integration with multimodal misinformation detection systems. Full article

Predicting forest trail visitation is essential for sustainable management and policy development, including infrastructure planning, safety operations, and conservation. However, due to numerous informal access points and complex external influences, accurately monitoring visitor numbers remains challenging. This study applied random forest, gradient boosting, and LightGBM models with Bayesian optimization to predict daily visitor counts across six sections of the National Daegwallyeong Forest Trail, incorporating variables such as weather conditions, social media activity, COVID-19 case counts, tollgate traffic volume, and local festivals. SHAP analysis revealed that tollgate traffic volume and weekends consistently increased visitation across all sections. The impact of temperature varied by section: higher temperatures increased visitation in Kukmin Forest, whereas lower temperatures were associated with higher visitation at Seonjaryeong Peak. COVID-19 cases demonstrated negative effects across all sections. By integrating diverse variables and conducting section-level analysis, this study identified detailed visitation patterns and provided a practical basis for adaptive, section- and season-specific management strategies. These findings support flexible measures such as seasonal staffing, congestion mitigation, and real-time response systems and contribute to the advancement of data-driven regional tourism management frameworks in the context of evolving nature-based tourism demand. Full article

The increased trend of globalization and the ever-growing world population have produced significant challenges to sustainable consumption goals, especially in the food industry. Production, transportation, and consumption of food have a major impact on sustainability. This bibliometric review aims to offer a comprehensive analysis of the influence of social media platforms on sustainable consumption in the food industry. Based on a literature search in the ISI Web of Science (WoS) database, we identified 38 documents by applying three filters: “sustainable consumption,” “food industry,” and “social media”, and a detailed screening process, a final set of 29 articles was selected for analysis. The selection criteria ensured relevance and alignment with the research objectives. We conducted a qualitative thematic analysis to identify emerging trends, aiming to highlight the potential of social media in raising awareness, cultivating sustainable consumption practices, and creating change in the food industry. The findings indicate that social media is a powerful tool not only for influencer marketing and brand communication but also for consumer empowerment and behavioral change. Our review identified key themes such as the prevalence of influencer-based food marketing, challenges related to misinformation, consumer demand for transparency, and the growing integration of big data and personalized marketing strategies. We argue that social media can significantly contribute to sustainability goals when responsibly used by marketers, educators, and policymakers. Full article

This study examines the scientific output on older adults’ use of social media by examining 2695 documents from the Web of Science database. These publications cover various areas, such as behavioral sciences, psychology, healthcare sciences, and computer sciences, and highlight institutions such as the University of Seville and the University of Michigan. The analysis was extended from 1995 to 2023, identifying 2012 as the year with the most significant increase. The United States led with 757 documents, followed by Spain with 511. Prominent authors include Jia Wu, Howard Giles, and Itai Himelboim. The study also highlighted frequent keywords such as “social networks, communication, and media”. A total of 41 countries were mentioned, and journals such as Profesional de la Información and Comunicar were analyzed. The historical perspective reveals theoretical connections between health, communication, and social networks in the elderly. Despite various challenges, such as the digital divide, the positive impact of social networks on the elderly stands out, providing a training demand for media education as an essential for all. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article

The 2010 Haiti earthquake stands as one of the most catastrophic events in terms of loss of life and destruction. Following an earthquake, there is an urgent demand for information. Regrettably, few studies have tracked the progress of the post-disaster recovery, leaving this phase poorly understood. In previous years, data were exclusively collected through on-site missions, but today, social media (SM) has enhanced earthquake reconnaissance teams’ capacity to collect data beyond the emergency phase. However, text data from SM is unstructured, making it necessary to use natural language processing techniques to extract meaningful information. Sentiment analysis (SA), which classifies people’s opinions into positive, negative, or neutral polarity, is a promising tool for understanding earthquake recovery. For the purposes of this paper, we conduct SA at the tweet level on data collected around the tenth anniversary of the earthquake using human expertise to fine-tune automatic classification methods. We conclude that the anniversary date is the best time to collect data. In our sample, 56.3% of the tweets in the sample were classified as negative, followed by positive (27.3%), neutral (8.2%), and unrelated (8.1%). In our study, we conclude that the assessment of the recovery progress based on data collected from Twitter is negative. The automatic method for SA with the highest accuracy is ‘btweet’. The assessment result must be validated by stakeholders. Full article

The increasing digitalisation of housing markets has expanded the types and sources of data available for research. However, there is limited understanding of how these diverse data types are used across different themes in urban housing studies and which analytical approaches are applied. This study addresses these questions through a systematic review of 71 peer-reviewed studies published between 2010 and 2021, following PRISMA guidelines. The review identifies five dominant research themes: housing market analysis, rental market analysis, housing policy evaluation, housing affordability, and housing inequality. It also classifies five main data sources: official statistics, non-official statistics, surveys and qualitative data, big data, and social media. A cross-examination of themes and data types shows that official statistics remain the most frequently used across the themes, while emerging data sources such as big data and social media are underutilised—especially in research on informal housing and demand-side dynamics. Regression analysis and hedonic modelling are the most commonly applied analytical methods, with the choice of method largely shaped by research objectives and data types. By developing a cross-typology framework linking research themes, data sources, and methods, this study provides an evidence base for inclusive, responsive, and data-informed strategies that support socially and economically sustainable urban housing systems. Full article

This study examines the role of creative resistance, or “artivism”, in Nigeria’s #EndSARS movement, a youth-led campaign against police brutality that peaked in October 2020. Drawing on Robert Entman’s Framing Theory, it analyzes how different art forms reframed public perceptions of the Special Anti-Robbery Squad (SARS) and countered government efforts to delegitimize the protests. Using a qualitative approach, the research employs purposive sampling of Twitter-sourced art forms to explore how these pieces exposed systemic injustice, amplified protester voices, and mobilized local and global support. Findings reveal that artivists personalized SARS brutality, dismantled narratives portraying protesters as criminals, and invoked moral urgency through evocative symbolism, leveraging social media’s virality to sustain the movement’s momentum. The study highlights SARS’ paradoxical role as a state-sanctioned yet reviled entity, demonstrating how creative expressions clarified this ambiguity into a clarion call for reform. By situating #EndSARS within Nigeria’s protest legacy, this analysis underscores art’s transformative power in digital-age activism, offering a blueprint for resistance against oppression. It contributes to scholarship on social movements by illustrating how art and technology intersect to challenge power, preserve collective memory, and demand accountability, with implications for future struggles in Nigeria and beyond. Full article

With the development of the economy and improvements in living standards, public demand for sports activities has continued to increase. However, the supply–demand relationship of urban sports venues remains unbalanced in many cities. Existing theoretical research on the spatial allocation of sports venues predominantly focuses on macro-level functional configuration and the equitable distribution of sports resources, lacking more rigorous and quantitative evaluation frameworks for evaluating spatial allocation. This study innovatively integrates multi-source data into the assessment and sustainable optimization of sports venue allocation in urban planning, using Xi’an as a case study. By analyzing geographic information, road network topology, OpenStreetMap (OSM), population distribution, and social media Points of Interest (POI), and using analytical tools such as ArcGIS 10.8 and Stata 17, the appropriateness of resource distribution of public sports venues in Xi’an’s main urban area is evaluated from three dimensions: accessibility, equity, and spatial activity. The results reveal the appropriateness of venue distribution in urban spatial allocation, the equitable distribution of resources, and imbalances in spatial activity and resource distribution. Finally, the study proposes a series of sustainable optimization strategies, including increasing venue coverage in low-supply areas, adaptive reuse of idle industrial buildings into sports venues guided by green sustainability principles, constructing a “15-min fitness circle” spatial system, optimizing low-carbon mobility networks around venues, enhancing the compatibility of sports venues, and improving commercial operation and management capabilities. These strategies aim to optimize the distribution of public sports venues in Xi’an to improve fairness and operational efficiency in service delivery while promoting sustainable urban development. Full article

The Philippines’ new religious movements (NRMs) emerged in the context of the rise of the religious nationalism movement and gradually flourished during the martial law period in the 1970s. Compared with traditional Catholicism, the theology of NRMs is more realistic and temporal, therefore creating an inherent demand to become politicized. After the People Power Movement, changes in the social environment, media technology, and electoral system in the Philippines created conditions for NRM groups to participate in politics more extensively and directly. They intervened in the political process through various means, such as bloc voting and running for public positions, with characteristics such as opposition to the Catholic Church, proactive and pragmatic political strategies, grassroots appeals, and a transnational mass base. The participation of NRM groups in politics has impacted the Catholic Church’s transcendental political status, enriched the political ecology dominated by oligarchic families, improved public welfare, and provided new channels for the voice of the grassroots. Overall, the rise of NRMs has not only changed the religious landscape of the Philippines but also profoundly affected its democratization process as an important factor, especially in the coming 2025 election. Full article