Search Results (618)

This article theorises Hmong migration through the concept of resettled histories, understood as the social and political processes through which histories of war, flight, loss, survival, and belonging are carried into new places, translated into new institutional languages, and contested across generations. Rather than approaching Hmong mobility as a linear movement from Southeast Asia to Western resettlement countries, the article situates Hmong migrations within longer histories of upland mobility, imperial and colonial governance, Cold War militarisation, refugee camps, and contemporary diasporic reconstruction. In response to scholarship on collective memory, postmemory, diasporic memory, refugee critique, digital diaspora, and Asian American studies, the article clarifies that resettled histories are not simply memories preserved after migration. They are histories that become socially active after resettlement through community organisations, veteran memorialisation, Hmong New Year celebrations, oral-history projects, clan networks, digital platforms, political mobilisation, and intergenerational debate. The article also foregrounds differences among Hmong communities by gender, generation, religion, class, political position, national location, and relations to Laos, China, Thailand, France, Australia, and the United States. By engaging contemporary Hmong scholarship and Hmong-produced archives alongside foundational migration and memory theory, the article shows how displaced communities do not merely adapt to host societies; they also struggle to have their histories recognised, narrated, and transmitted. The Hmong case demonstrates that resettlement may provide legal security without ending the historical life of displacement. It therefore offers a lens for rethinking migration as the movement and reconstruction of histories, not only the movement of people. Full article

Agricultural sustainability assessment is increasingly required to characterize how food production systems interact with land, soil, water, carbon dynamics, and broader environmental change. However, the extent to which scientific assessment methods capture these environmental-system interactions remains unclear. This study mapped methodological and thematic trends in agricultural sustainability research through text mining of 3302 bibliographic records retrieved from the Web of Science Core Collection, which was selected because of its standardized metadata structure and suitability for reproducible text-mining analysis, covering publications from 2003 to 1 March 2025. After corpus preprocessing and tokenization, term-frequency analysis, dimension-specific lexical classification, co-occurrence networks, and temporal bibliometric trends were used to identify dominant environmental themes and assessment approaches. The results revealed a clear predominance of the environmental dimension in the analyzed literature, particularly through terms associated with land, carbon, soil, and water resources, whereas social and economic dimensions displayed lower lexical representation. Food, production, and systems formed a central semantic cluster linking environmental assessment with food security. Life Cycle Assessment (LCA) was the most frequently identified methodology, reflecting the prominence of impact-oriented environmental evaluation. In contrast, integrative and farm-scale frameworks, including Driver–Pressure–State–Impact–Response (DPSIR), Sustainability Assessment of Food and Agriculture Systems (SAFA), and the Tool for Agroecology Performance Evaluation (TAPE), among others, indicated increasing attention to governance, resilience, and agroecological transitions. These findings show that text mining can support environmental research by identifying methodological biases and emerging priorities in agriculture–environment interactions. Strengthening integrated assessment approaches will be essential for managing natural resources and supporting resilient and environmentally sustainable food systems. Full article

Agri-food systems are increasingly exposed to environmental, economic, and social challenges, including climate change, biodiversity loss, resource depletion, and growing territorial inequalities. In this context, agroecology is increasingly recognised as a transformative paradigm integrating ecological, economic, social, cultural, and political dimensions within broader processes of food-system transition. Within the PRIMA AgrEcoMed project, 24 Italian agroecological initiatives led by women and young farmers were analysed to explore their contribution to agroecological transition processes in Mediterranean rural areas. The study adopts a qualitative multiple-case study approach and evaluates the selected initiatives through the framework of the 13 Principles of Agroecology proposed by the High-Level Panel of Experts on Food Security and Nutrition, organised into three operational axes: improving resource efficiency, strengthening resilience, and ensuring social responsibility and fairness. The results show that the analysed initiatives combine ecological farming practices with processes of multifunctionality, territorial networking, knowledge co-creation, short supply chains, and community engagement. The findings suggest that several initiatives move beyond input-reduction strategies associated with “weak agroecology” and display characteristics consistent with stronger agroecological pathways based on territorial embeddedness, collective learning, and the reorganisation of relationships between production, consumption, and local communities. The paper highlights the relevance of agroecology not only as an environmentally sustainable farming approach, but also as a broader socio-ecological and territorial transition process, as well as the importance of policy frameworks to support territorial agroecological systems. Full article

Phishing remains a widespread and evolving cyber threat that targets human and technical vulnerabilities across email, web, mobile, and social media. Meanwhile, digital forensics has developed into a standards-driven discipline dedicated to identifying, preserving, analysing, and presenting digital evidence. Despite overlapping goals, phishing detection research and digital forensics typically operate separately. Detection efforts emphasise classification accuracy and rapid mitigation, while forensic practices prioritise evidential integrity and incident reconstruction. The analysis suggests that incorporating forensic-quality artefacts, such as Simple Mail Transfer Protocol (SMTP) headers, Domain Name System (DNS) and Transport Layer Security (TLS) traces, memory dumps, behavioural logs, metadata, and provenance records, may support attribution analysis, interpretability, and more evidentially robust incident reporting. It covers email, network, endpoint, behavioural, and legal areas to identify common shortcomings in forensic readiness, provenance preservation, and reproducibility. Based on these insights, we propose a conceptual framework that redefines digital forensics as a proactive, ongoing capability integrated into operational phishing defences. The review highlights gaps in research, such as the limited availability and validation of AI-generated phishing datasets, privacy-aware evidence management and deanonymization risks in evidence correlation, and automated workflows for handling evidence. It also suggests future directions for integrating forensic reasoning into advanced phishing mitigation systems. Full article

The Social Internet of Things (SIoT) has emerged as a promising paradigm for addressing interoperability, adaptability, and intelligent collaboration challenges in smart city environments. However, existing solutions often provide only partial support for semantic interoperability, dynamic social relationships, and context-aware service coordination across heterogeneous IoT ecosystems. This paper presents SEMIWARE, a semantic social network-oriented middleware designed to support collaborative, interoperable, and context-aware SIoT applications. SEMIWARE adopts a layered architecture that combines a FIWARE-based middleware backbone with modular services for context management, semantic annotation, semantic reasoning, service discovery, social relationship management, profiling, security, and ontology alignment. Its semantic backbone is provided by an OWL2 ontology that models IoT entities, users, services, contextual information, and trust-aware social relationships. The middleware is validated through two representative applications in distinct domains: smart mobility, where semantic reasoning supports adaptive eco-friendly route computation, and healthcare, where semantically integrated wearable and environmental data support health-event detection for people with dementia. Experimental evaluation further examines the performance of semantic annotation, semantic reasoning, and context management services under increasing workloads. The results provide prototype-level evidence that SEMIWARE supports semantic interoperability, cross-domain adaptability, and graph-based processing under controlled workloads, indicating its potential suitability for complex, data-intensive SIoT applications. Full article

Voice phishing, commonly known as vishing, has become one of the fastest-growing threats in social engineering. The rapid advancement and accessibility of AI voice cloning tools have enabled attackers to produce highly convincing synthetic speech at minimal cost, driving a sharp increase in impersonation fraud. Accordingly, automatic detection of synthetic voices could contribute, as one component of a broader defense, to mitigating vishing attacks. This paper studies the automatic detection of AI-generated speech, with a particular focus on how well such detectors generalize beyond their training data to modern, unseen synthesis methods. Two detection approaches are evaluated: a Residual CNN (convolutional neural network) trained as a binary classifier on three different time–frequency representations and a one-class learning strategy with a ResNet-18 backbone, yielding four models in total. Models were trained on the well-known ASVspoof 2019 Logical Access dataset and tested on its standard partitions. Then, models were tested on the SONAR benchmark, which gathers voices generated with state-of-the-art synthesis techniques unseen during training. Experimental results show that, on the modern systems gathered in SONAR, all four configurations fall close to chance. The LFCC one-class detector generalizes comparatively best, but the apparently higher accuracy of some models reflects a tendency to label most speech as spoofed. These findings indicate that the evaluated detectors can provide, at most, a partial security layer against vishing driven by current and emerging speech-synthesis technologies, although continuous model updates are recommended. Full article

The growth of advanced cyber threats has inspired organisations to start using powerful cybersecurity platforms, but the process of selection is analytically challenging due to the multidimensional, uncertain, and conflicting character of the evaluation criteria. The prevailing culture of decision-support frameworks is based on unyielding numerical evaluations that cannot reflect the underlying vagueness of expert judgment and the dynamic interplay of macro-environmental factors. This paper presents a combined Fuzzy Multi-Criteria Decision-Making (FMCDM) system, which uses polygonal fuzzy numbers, in particular pentagonal fuzzy representation, and four other complementary methods of MCDM (Fuzzy AHP, Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS), integrated by a Borda Count consensus system. Sixteen assessment sub-criteria are logically obtained through an analysis of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) and weighted using the Fuzzy Analytic Hierarchy Process. The model is used to compare six cybersecurity platforms, including Microsoft Security Framework, CrowdStrike Falcon, Cisco Cybersecurity Portfolio, Palo Alto Networks Cortex, Fortinet Security Fabric, and Sophos Central. In this study, Fuzzy AHP demonstrates that the aggregate weight of political factors is the highest (0.4181), followed by cross-border data management, regulatory compliance, and government incentives as the most popular sub-criteria. According to the results from the Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS methods, Microsoft Security Framework ranks consistently in the first place, and CrowdStrike Falcon and Cisco Cybersecurity Portfolio were ranked second and third, respectively. The framework presented in the study provides decision-makers with a reproducible, uncertainty-conscious basis for cybersecurity platform selection. Full article

This paper offers a theoretical reinterpretation of the groundbreaking Greentown study using Pierre Bourdieu’s Theory of Practice. Rather than presenting new empirical findings, it examines previously published research to study children’s involvement in organised crime networks through a relational, practice-based lens. Dominant approaches to youth offending and gang participation tend to focus on individual risk factors, programme effectiveness or structural indicators and can struggle to account for the enduring social logics through which criminal authority is reproduced across generations. Drawing on Bourdieusian concepts of field, capital and symbolic power, the paper interprets Greentown as a localised social field in which a core family network accumulates and deploys social, cultural, economic and symbolic capital to secure compliance, cultivate loyalty and sustain informal forms of governance. Attention is paid to the role of symbolic narratives and mythmaking in minimising the visible presence of the state and normalising participation for young people and residents. The analysis illustrates how such symbolic orders can persist even where individual agents desist, contributing to the relative stability of networked harm. The paper argues that Bourdieu provides a coherent and theoretically disciplined framework for understanding organised criminal networks as socially embedded fields and suggests that interventions attentive to symbolic power and misrecognition may complement existing criminal justice responses. While explicitly interpretive in scope, the paper points towards the value of theory-led re-readings of empirical research for addressing the complex and ‘wicked’ nature of organised networked offending. Full article

Botanical gardens are multifunctional institutions that perform a wide range of functions using limited resources. Their social significance has grown alongside global challenges like biodiversity loss, the climate crisis, and food security, as well as the need to train future professionals. This study analyzes public preferences for the diverse functions of botanical gardens to inform sustainable management strategies, using the Sejong National Arboretum in South Korea as a case study. We identified seven attributes, including five traditional functions (collection, conservation, research, exhibition, and education) and two extended functions (healing and networking) that reflect the contemporary roles of botanical gardens. We conducted a discrete choice experiment in 2024 with 1200 respondents to assess preferences and marginal willingness to pay. Respondents showed the strongest preferences for research commercialization and global conservation, followed by urban outreach in healing. In contrast, we observed negative marginal willingness to pay values for exhibition, professional certification, national networking, and nationwide outreach in healing. Our findings indicate that the public interest in botanical garden activities extends beyond visitor-oriented functions to include conservation, research commercialization, and locally embedded healing services. These results offer an empirical basis for resource allocation and sustainable management strategies in botanical gardens. Full article

Indigenous food systems are increasingly threatened by climate change, socio-economic transformations, and reduced access to traditional lands and resources, contributing to disproportionately high levels of food insecurity among Indigenous peoples. Despite growing recognition of Indigenous food systems within sustainability research, limited attention has been given to Indigenous food system governance across different contexts. This study examined: (1) how Indigenous food systems vary across continents; (2) the key characteristics of Indigenous food system governance; and (3) how these characteristics are expressed within Sri Lankan Vedda communities. A systematic literature review of 143 publications from Web of Science and Scopus was conducted alongside a multi-sited case study involving 114 semi-structured interviews across six Vedda communities in Sri Lanka. Findings revealed continental variations in food sourcing, food sources, food use, and harvesting practices. Eight interconnected governance characteristics were identified: co-management, leadership, participatory research, partnerships, social networks, mutualism, collective action, and religious/cultural dimensions. Evidence from Sri Lankan Vedda communities demonstrated that strong leadership, social cohesion, and collaborative partnerships enhanced food security and resilience, whereas weakened governance structures and limited external support contributed to food insecurity. The study highlights the importance of strengthening Indigenous self-governance to support sustainable Indigenous food systems. Full article

China’s low-altitude economy (LAE) is moving from policy experimentation to coordinated industrial deployment, yet existing assessments often treat the LAE as a homogeneous sector or equate aircraft capability with deployment readiness. This study develops a scenario-gated sustainability readiness framework for six representative LAE and urban air mobility (UAM) scenarios in China: emergency medical logistics and disaster response, infrastructure inspection and public-service monitoring, urban instant logistics, airport shuttle and intermodal passenger transfer, urban air taxi, and low-altitude tourism. The proposed framework consists of a scenario layer, an eight-dimensional readiness layer, and a decision layer integrating 0–4 ordinal scoring, evidence-confidence tagging, non-compensatory gate conditions, and readiness classification. The eight dimensions cover mission and demand fit; airspace and traffic controllability; infrastructure and site readiness; digital communication, navigation, surveillance, and data security; vehicle, energy, and environmental performance; weather and route-environment robustness; workforce and organizational readiness; and social acceptance and legal legitimacy. The illustrative application indicates that infrastructure inspection is the only routine scaling candidate; emergency medical logistics and urban instant logistics are suitable for bounded routine operation; airport shuttle and tourism should remain controlled pilot candidates; and open-network urban air taxi is still at the pre-pilot stage. The study contributes a scenario-based deployment logic for sustainable aviation and UAM governance. Full article

Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

As urbanization picks up pace and the public demand for security keeps climbing, video surveillance systems have emerged as a vital tool for maintaining social stability and safeguarding public safety. Person Re-Identification (Re-ID), as one of the core technologies in intelligent monitoring, mainly aims to accurately match pedestrian identities across cameras without overlapping fields of view. However, in practical applications, occlusion remains a primary challenge that severely degrades Re-ID performance. Especially in high-density crowds, pedestrians are often partially or completely obscured by other objects or individuals, resulting in incomplete image information and impaired feature representation, which significantly reduces recognition accuracy and reliability. Aiming at the problems of excessive reliance on external pose estimation models and asymmetric information matching in occluded Re-ID, this paper proposes a transformer-based pedestrian background decoupling network. The algorithm achieves foreground–background separation and multi-scale feature matching through the synergy of three modules. Meanwhile, a two-stage training strategy is adopted: the first stage optimizes the decoupling module to ensure clean feature separation, while the second stage jointly fine-tunes the correlation module to enhance matching accuracy. Extensive experimental results show that the proposed algorithm outperforms existing methods. Full article

In recent years, cyberspace governance has become a critical component of national security strategies worldwide. Although social network platforms provide users with convenient channels for expression and information acquisition, unregulated, harmful euphemisms have become increasingly prevalent. These euphemisms disrupt the order of the digital space and trigger secondary harms such as cyberbullying and regional discrimination. Currently, researches on Chinese harmful euphemism detection face three key challenges: the lack of large-scale annotated datasets, the cognitive reasoning deficit in lightweight models, and the latency constraints of Large Language Models (LLMs), which collectively constrain detection performance and real-world generalization. To address these issues, this study first collected a large corpus from social networking platforms and constructed a fine-grained annotated harmful euphemism dataset. Then, a representation learning framework was designed by integrating deep prompt-based chain-of-thought reasoning with multi-head contrastive learning. This framework introduces external knowledge from LLMs to enhance the diversity and precision of semantic representations. Finally, a multi-dimensional semantic perception fusion framework was proposed. It incorporates multiple semantic perception channels and a cross-channel dynamic fusion mechanism, enabling the model to better capture implicit semantics and integrate external contextual knowledge. Experimental results show that our approach significantly outperforms state-of-the-art lightweight models. While large-scale LLMs exhibit superior zero-shot transferability in cross-domain tasks, our proposed model maintains highly competitive performance with substantially lower inference latency and computational overhead. This research provides a novel methodological and technical foundation for detecting harmful euphemisms in social networks. Full article