Search Results (231)

This study analyzes how Vietnam’s Ministry of Culture, Sports and Tourism (MoCST) communicates policy implementation in tourism governance through 35 official responses to citizen petitions, using Heidbreder’s Multilevel Policy Implementation Strategies Framework (centralization, agencification, convergence, networking). Content coding, frequency analysis, co-occurrence network, and sentiment analysis reveal a dominant centralization pattern, with MoCST maintaining strong top-down control in decision-making and resource allocation. Convergence reflects increased inter-ministerial coordination, while agencification is limited, and networking with private or civil sectors remains weak. This weak networking limits participatory decision-making and hinders the development of adaptive, community-based tourism initiatives, which are crucial for sustainable tourism governance. Positive sentiment is more associated with centralized and convergent actions, indicating institutional trust. The study extends Heidbreder’s framework to a non-Western, centralized context and calls for stronger local agency roles and inclusive networks to enhance resilience and community ownership in policy implementation. Full article

This article investigates the political cost of poverty in democracies marked by deep social divisions. We develop a probabilistic voting model that incorporates clientelism as a strategic tool employed by elite political parties to secure electoral support from non-elite voters. Unlike models based on ideological proximity, our framework conceptualizes party competition as structured by the socioeconomic composition of their constituencies. We demonstrate that in contexts of high inequality and widespread poverty, elite parties face structural incentives to deploy clientelistic strategies rather than universalistic policy agendas. Our model predicts that clientelistic expenditures by elite parties increase proportionally with both inequality (GINI index) and poverty levels, rendering clientelism a rational and cost-effective mechanism of political control. Empirical evidence from a cross-national panel (2013–2019) confirms the theoretical predictions: an increase of the 1 percent in the GINI index increase a 1.3 percent in the clientelism, even after accounting for endogeneity and dynamic effects. These findings suggest that in divided democracies, poverty is not merely a condition to be alleviated, but a political resource that elites strategically exploit. Consequently, clientelism persists not as a cultural residue or institutional failure, but as a rational response to inequality-driven constraints within democratic competition. Full article

Quantum-dot cellular automata (QCA) offer a high-performance, low-power alternative to traditional VLSI technology for nanocomputing. However, the existing metal-dot QCA-based parity generators and checker circuits suffer from increased energy dissipation, larger area consumption, and complex multilayered layouts, limiting their practical feasibility. This work designs a 3-bit parity generator and 4-bit checker to address these challenges using an optimized modified majority voter-based Ex-OR gate in QCA. A single-layered layout was simulated in QCADesigner 2.0.3, avoiding crossovers to reduce fabrication complexity. Energy analysis using QCADesigner-E reveals 34.4 meV energy consumption, achieving 31% energy efficiency and 75% area efficiency in the context of QCA costs compared to recent designs. The proposed circuit highlights the unique potential of QCA as a scalable, energy-efficient solution for high-density next-generation computing systems. Full article

Scholars and journalists typically trace the diffusion of breed-specific legislation (BSL) in the U.S. to the surge in negative media portraits of pit bull-type dogs (PBTDs) during the late twentieth century. Yet, while news coverage still portrays these dogs unfavorably, we document a sharp rise in countervailing sources of “pit bull positivity” over the past two decades. Drawing on insights from the respective social science research on changes in attitudes and public policy, we argue that this influx of positivity should powerfully impact opinions and policies towards PBTDs. Our data and analyses consistently support that argument. We analyze two different series of repeated cross-sectional surveys to show that public support for “pit bulls” grew considerably from 2014 to 2024. We also show that voters’ support for ballot measures overturning local “pit bull bans” increased substantially during that same ten-year period. Finally, our analysis of the frames and narratives deployed in recent state and local policy debates shows how this growing pit bull positivity has helped overturn over 300 discriminatory laws against these dogs since 2012. We conclude with a discussion of how shifts in portraits and opinions of PBTDs will likely continue eroding breed-specific legislation going forward. Full article

Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. This study evaluates several heuristics to address an allocation problem involving young individuals reaching voting age. A five-step methodology was implemented: defining variables, executing heuristics, compiling results, evaluating outcomes, and selecting the most effective heuristic. Using experimental data from the Mexican National Electoral Institute (INE), the study focuses on 88,107 individuals aged 17–18 in the 16 municipalities of the Toluca Valley, who can access any of the 10 INE service modules. Six heuristics were analyzed in sequence: genetic algorithm, ant colony optimization, local search, tabu search, simulated annealing, and greedy algorithm. The results indicate that genetic algorithm significantly reduces the processing time when used as the initial heuristic. Furthermore, given the current capacity of the 10 INE modules, serving the entire target population would require nine working days. These findings align with principles of spatial justice and highlight the practical efficiency of heuristic-based solutions in administrative resource allocation. The main contribution of this study is the development and evaluation of a hybrid heuristic framework for allocating INE modules, demonstrating that combining multiple heuristics—with a genetic algorithm as the initial phase—significantly improves solution quality and computational efficiency. Full article

This study proposes an AI-assisted governance framework to enhance decision-making within decentralized autonomous organizations (DAOs). By integrating chain-of-thought (CoT) reasoning with stakeholder-adaptive recommendations, the framework improves decision alignment, increases voter participation, and enhances governance transparency. Through simulations based on historical KlimaDAO data, the system achieved a 97% alignment with past decisions, a projected 40% increase in participation, and a 35% improvement in governance clarity. To support quantitative analysis in tokenomics, we developed a tailored CoT reasoning strategy, effectively reducing information asymmetry and generating structured, trustworthy recommendations. These results underscore the potential of AI to foster more inclusive and transparent DAO governance. Future work will explore deploying lightweight AI models and extending this approach to a broader range of DAO ecosystems. Full article

In many areas of operation, application-specific logic implemented in FPGAs (Field Programmable Gate Arrays) is critical. In these situations, various mitigation methods are used to reduce or completely eliminate malfunctions in the circuit resulting from undesired physical phenomena (e.g., ionizing radiation). Such phenomena may occur, among others, in medicine, the military, nuclear power, and space systems. One of the most popular methods is the use of triple modular redundancy (TMR). Here, the FPGA provides a good basis for building TMR-based safety-critical systems due to its concurrent processing. This paper presents an overview of the implementation of logic structures using TMR. In this paper, the authors focus on different concepts for the implementation of FSMs. The different concepts differ in the way TMR voters are attached and the extent of redundancy of the individual FSM components. The article compares the efficiency of the different solutions. In order to evaluate this efficiency, it is crucial to determine the logic utilization or the power consumption of a given implementation. In the experimental part of the article, the authors show the results of the synthesis of FSM benchmarks, for different mitigation models. The synthesis was carried out for both commercial and academic tools. Full article

Architectural ambiance refers to the mood perceived in a built environment, assessed through human reactions to virtual drawings of prospective spaces. This paper investigates the use of a ready-made artificial intelligence model to automate this task. Based on professional BIM models, videos of virtual tours of typical urban areas were built: a business district, a strip mall, and a residential area. GPT-4V was used to assess the aesthetic quality of the built environment based on keyframes of the videos and characterize these spaces shaped by subjective attributes. The spatial qualities analyzed through subjective human experience include space and scale, enclosure, style, and overall feelings. These factors were assessed with a diverse set of mood attributes, ranging from balance and protection to elegance, simplicity, or nostalgia. Human participants were surveyed with the same questions based on the videos. The answers were compared and analyzed according to these subjective attributes. Our findings indicate that, while GPT-4V demonstrates adequate proficiency in interpreting urban spaces, there are significant differences between the AI and human evaluators. In nine out of twelve cases, the AI’s assessments aligned with the majority of human voters. The business district environment proved more challenging to assess, while the green environment was effectively modeled. Full article

Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability. Full article

Despite extensive scholarship on social media political party strategies or intra-party-political campaigns across digital platforms, it remains relatively unexplored how individual presidential candidates adopt social media to frame their messages on key political issues for voter engagement, especially in the West Africa region. To fill this gap, this study examines how the two major presidential candidates in Ghana, John Mahama of the National Democratic Congress (NDC) and Mahamudu Bawumia of the New Patriotic Party (NPP), use social media platforms to frame key political issues during the 2024 election campaign. Using framing theory and digital multimodal discourse analysis as the conceptual and methodological frameworks, the study examines content on X (formerly Twitter), Facebook, and Instagram, with a focus on issues related to the economy and education, while also assessing how platform-specific affordances shape the presentation and visibility of these frames. The findings of the study reveal three core dynamics in the framing strategies of both candidates: (1) contrasting economic narratives (‘Resetting Ghana’ vs. ‘It Is Possible’), (2) competing visions of education (reform vs. continuity), and (3) platform-specific engagement patterns. These findings offer insight into how political actors leverage digital affordances beyond simple messaging tools into structured framing mechanisms and strategically construct narratives to shape public discourse and influence voter engagement. Full article

This study evaluates the link between economic voting and electoral behavior in the 2024 European Parliament (EP) elections. This study is grounded in both selective perception and economic voting theories, examining how different independent factors could interact with electoral behavior. In this regard, the research aims to achieve several research directions: (i) the evaluation of the statistical differences in voters’ turnout in 2024 EP elections by geographical regions; (ii) the analysis of the interaction between voters’ perceptions of the current or future economic situations and voter turnout; (iii) the analysis of the interaction between objective economic conditions and electoral behavior. Using both multiple linear regression and logistic models, the study highlights that voter turnout and incumbent party reelection are significantly related to voters’ perceptions of the current or future state of the national economy. The results reveal that regional differences in voter turnout are largely explained by significant differences in voters’ economic perceptions, while the decision to vote for the incumbent party is driven by future economic expectations. The empirical findings underscore the pivotal role played by subjective perceptions in shaping electoral behavior, illustrating that political attitudes and behaviors are derived from personal interpretation of the national economic situations. Beyond theoretical perspectives that highlight the link between psychological processes and voting, the paper might have several practical implications for academics or decision makers interested in the field of electoral behavior. Full article

In electronic voting systems, a large number of voters are often required to vote. It is also necessary to ensure the security of the voters and the fairness of the vote. The use of ring signatures is very suitable for e-voting systems because of their special anonymity. Among the many types of ring signatures, certificateless ring signature (CRS) stands out because it does not require certificates and avoids the need to completely trust the key generation center (KGC). In this paper, we propose a certificateless ring signature based on the special structure of the number theory research unit (NTRU) lattice, which utilizes the Merkle tree and seed tree to split commitments and integrate them again to generate signatures. At the same time, we embed the NTRU small integer solution (NTRU-SIS) problem and provide a detailed proof of security under the random oracle model (ROM). In efficiency, the Merkle tree makes the signature size logarithmically increase with the ring scale. In the era of big data explosion, this feature enables the proposed scheme to maintain a comparatively short signature size even when the number of ring members N is very large. When $N=8$, the signature size is 61.08 KB; when N increases to 512, the size is 65.02 KB. From the data, we can observe that the signature size grows slowly, by only 4 KB when N grows exponentially, which is much slower than ring signatures with linear growth. Full article

This study analyzes 194,151 tweets from the 2021 German federal election using sentiment analysis and statistical techniques to examine social media’s role in shaping group emotions, voters’ emotional expression and derogatory speech toward candidates, and the relationship between sentiment intensity and tweet spread. The findings show that negative emotions dominated social media discussions. Additionally, voter perceptions towards candidates on social media also follow a pattern of negativity, often characterized by derogatory speech. This takes four main forms: intelligence-based attacks, animal metaphors, character insults, and gender-based discrimination, with female candidates disproportionately affected. Moreover, the study finds that negative emotions exhibit significantly greater diffusion and reach compared to positive and neutral sentiments on social media. This study further examines election fairness and political dialog openness through the lens of equity, inclusion, diversity, and access (IDEA). These findings emphasize individual and collective emotional dynamics in the social media environment, highlighting the need for governance strategies that promote equity, inclusivity, and diversity in digital political discussions. Full article

Research on climate activism has predominantly focused on affluent regions of the Global North, often emphasizing public participation and protest while overlooking the experiences of youth in other contexts. This study addresses this gap by exploring everyday environmental activism and eco-literacy among young people in Bulgaria, a post-communist society. It challenges the prevailing top-down political frameworks that marginalize diverse forms of political participation. This study argues that young people’s environmental awareness, shaped by their lived experiences, reflects their engagement with consumerism and climate change and is expressed through various modes of participation, including the victim, voter, rejecter, and interpreter forms of agency. Drawing on ethnographic data from interviews, mapping activities, and short essays, this research examines how environmental identities are enacted in mundane ways that reflect young people’s levels of eco-literacy, focusing on a cohort from diverse socioeconomic backgrounds in both urban and rural contexts. The findings reveal the performances of everyday environmental activism and shed light on the liminal political positions youth navigate in their daily lives. This research contributes to education studies by offering insights into how young people’s everyday environmental activism and eco-literacy, rooted in their subjectivities, transcend traditional educational frameworks and provide a deeper understanding of how they learn to become environmental subjects in under-represented contexts. Full article

This study analyses the emotional communication strategies employed by far-right party leaders on TikTok during the 2024 European elections, focusing on their appeal to voters. Combining quantitative and qualitative content analysis of 472 videos from 27 leaders in 24 countries, the research examines the dominant emotions, themes, stylistic resources, and their impact on engagement. The results reveal a dual strategy that combines positive emotions such as hope and ambition, which generate the highest levels of engagement, with negative emotions such as fear and uncertainty, which emphasise crisis narratives. Leaders who balance optimism with polarising narratives show greater resonance, particularly with audiences. The findings underscore the growing role of TikTok in far-right political communication and demonstrate its effectiveness in mobilising emotional engagement among young people. Despite limitations, the study highlights the sophistication of emotional strategies in digital political communication and provides insights into how far-right leaders use TikTok to influence voter behaviour. Full article