Search Results (444)

The implementation of the trans-European tourist cycle route network “EuroVelo” is fostering new strategic importance for non-motorized mobility and the associated practice of cycling tourism. Indeed, slow tourism offers a pathway for the development of inland areas. The infrastructure supporting it, such as long-distance cycling and walking paths, can act as a vital connection, stimulating regeneration in peripheral territories by enhancing environmental and landscape assets, as well as preserving heritage, local identity, and culture. The regeneration of peri-urban landscapes through soft mobility is recognized as the cornerstone for accessibility to material and immaterial resources (including ecosystem services) for multiple categories of users, including the most vulnerable, especially following the restoration of green-area systems and non-urbanized areas with degraded ecosystems. Considering the forthcoming implementation of the Magna Grecia cycling route, the southernmost segment of the “EuroVelo” network traversing three regions in southern Italy, this contribution briefly examines the necessity of defining new development policies to effectively integrate sustainable slow tourism with the enhancement of environmental and landscape values in the coastal areas along the route. Specifically, this case study focuses on a coastal stretch characterized by significant morphological and environmental features and notable landscapes interwoven with densely built environments. In this area, environmental and landscape values face considerable threats from scattered, irregular, low-density settlements, abandoned sites, and other inappropriate constructions along the coastline. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article

This paper introduces the concept of degenerative affordances to explain how social media can unintentionally destabilise family-run influencer businesses. While affordance theory typically highlights the enabling features of technology, the researchers shift the focus to its unintended, risk-laden consequences, particularly within family enterprises where professional and personal identities are deeply entangled. Drawing on platform capitalism, family business research, and intersectional feminist critiques, the researchers develop a theoretical model to examine how social media affordances contribute to role confusion, privacy breaches, and trust erosion. Using a mixed-methods design, the researchers combine narrative interviews (n = 20) with partial least squares structural equation modelling (PLS-SEM) on survey data (n = 320) from family-based influencers. This study’s findings reveal a high explanatory power (R² = 0.934) for how digital platforms mediate entrepreneurial legitimacy through interpersonal trust and role dynamics. Notably, trust emerges as a key mediating mechanism linking social media engagement to perceptions of business legitimacy. This paper advances three core contributions: (1) introducing degenerative affordance as a novel extension of affordance theory; (2) unpacking how digitally mediated role confusion and privacy breaches function as internal threats to legitimacy in family businesses; and (3) problematising the epistemic assumptions embedded in entrepreneurial legitimacy itself. This study’s results call for a rethinking of how digital platforms, family roles, and entrepreneurial identities co-constitute each other under the pressures of visibility, intimacy, and algorithmic governance. The paper concludes with implications for influencer labour regulation, platform accountability, and the ethics of digital family entrepreneurship. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals. Full article

Brucellosis remains endemic in Tunisia, causing abortions in small ruminants, and represents a public health threat through occupational exposure and the consumption of contaminated animal products. The aims of this study are to assess the antibiotic susceptibility of two Brucella melitensis isolates (TATA and SBZ) from aborted sheep, to analyze their genomes using hybrid whole-genome sequencing, and to investigate their antimicrobial resistance (AMR), potential virulence factors (VFs), and phylogenetic relationships. Both isolates were phenotypically confirmed to be susceptible to doxycycline, gentamicin, rifampicin, streptomycin, and trimethoprim–sulfamethoxazole, and no corresponding classical AMR genes were identified. However, several potential AMR-related genes (mprF, bepCDEFG, qacG, and adeF) and a mutation in the parC gene were detected. The analysis of the genotypes revealed 74 potential virulence genes, primarily involved in lipopolysaccharide synthesis and type IV secretion systems. Genomic comparison showed over 99% nucleotide identity between the Tunisian strains, B. melitensis bv. 1 16M and B. melitensis bv. 3 Ether. Five gene clusters, including three hypothetical proteins with 100% identity, were detected exclusively in the TATA and SBZ strains. Additionally, two unique gene clusters were identified in SBZ: a rhodocoxin reductase and another hypothetical protein. Both isolates were assigned to sequence types ST11 and ST89. Core-genome-based phylogenetic analysis clustered both strains with biovar 3 and ordered the Tunisian strains into two distinct groups: TATA within Tunisian Cluster 1 is closely related to strains from Egypt and Italy, while SBZ near MST Cluster 4 is more related to isolates from Austria and two outliers from Italy and Tunisia. This study provides the first genomic characterization of B. melitensis from aborted sheep in Tunisia and offers valuable insights into AMR, virulence, and phylogenetic distribution. Full article

Rural landscapes play a key role in preserving ecological processes, cultural identity, and socio-economic well-being, yet these areas often face challenges such as land degradation, water scarcity, and an inadequate road network. A sustainable approach to rural landscape and tourism planning is essential for enhancing both environmental resilience and socio-economic vitality in areas facing degradation and global change. This study aims to develop and validate an integrated methodological workflow that combines Landscape Character Assessment (LCA), ECOVAST guidelines, SWOT analysis, and open-source GIS techniques, complemented by a bottom-up approach of spontaneous fruition mapped through Wikiloc heatmaps. The framework was applied to a case study in Paternò, Eastern Sicily, Italy—a territory distinguished by its key local values such as Calanchi formations, proximity to Mount Etna, and cultural heritage. Through this application, eight distinct Landscape Units (LUs) were delineated, and key strengths, weaknesses, opportunities, and threats for sustainable development were identified. Using open-access data and a survey-free protocol, this approach facilitates detailed landscape assessment without extensive fieldwork. The methodology is readily transferable to other rural Italian and Mediterranean contexts, providing practical guidance for researchers, planners, and stakeholders engaged in sustainable tourism development and landscape management. Full article

This article presents a social and historical analysis of Barranco district in Lima, Peru, examining its evolution as a postmodern tourism destination. Through documentary analysis of historical records, guidebooks, press articles, and novels, we investigate the significant social milestones along history with cultural, and economic impacts on tourism development in Barranco. The research employs a methodology to identify key elements that have shaped Barranco’s trajectory from a fishing settlement to a bohemian district in a lively city and finally to its status as a “hipster” neighbourhood. Drawing on postmodern tourism theory and Hawley’s socioeconomic development theory, we argue that Barranco represents a distinctive case of how cultural capital transforms and resignifies tourism spaces through complex identity processes. Findings reveal that Barranco’s development follows three clear phases: rural settlement (until the 19th century), seaside resort (mid-19th century to 1940s–60s), and urban district with postmodern tourism appeal (1990s onward). The study concludes that while Barranco’s bohemian and artistic identity has become a distinctive tourism asset, the district faces gentrification challenges that threat its sociocultural diversity and authenticity. This research contributes to understanding how postmodern tourism influences territorial identity transformation and illuminates the social, historical, and economic forces that shape distinctive urban tourism destinations. Full article

Advancements in Internet of Things (IoT) technologies have had a profound impact on interconnected devices, leading to exponentially growing networks of billions of intelligent devices. However, this growth has exposed Internet of Things (IoT) systems to cybersecurity vulnerabilities. These vulnerabilities are primarily caused by the inherent limitations of these devices, such as finite battery resources and the requirement for ubiquitous connectivity. The rapid evolution of deep learning (DL) technologies has led to their widespread use in critical application domains, thereby highlighting the need to integrate DL methodologies to improve IoT security systems beyond the basic secure communication protocols. This is essential for creating intelligent security frameworks that can effectively address the increasingly complex cybersecurity threats faced by IoT networks. This study proposes a hybrid methodology that combines fractional discrete Tchebichef moment analysis with deep learning for the prevention of IoT attacks. The effectiveness of our proposed technique for detecting IoT threats was evaluated using the UNSW-NB15 and Bot-IoT datasets, featuring illustrative cases of common IoT attack scenarios, such as DDoS, identity spoofing, network reconnaissance, and unauthorized data access. The empirical results validate the superior classification capabilities of the proposed methodology in IoT cybersecurity threat assessments compared with existing solutions. This study leveraged the synergistic integration of discrete Tchebichef moments and deep convolutional networks to facilitate comprehensive attack detection and prevention in IoT ecosystems. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article

The widespread adoption of web applications has led to a significant increase in the number of automated cyberattacks. Parameter tampering attacks pose a substantial security threat, enabling privilege escalation and unauthorized data exfiltration. Traditional pattern-based detection tools exhibit limited efficacy against such threats, as identical parameters may produce varying response patterns contingent on their processing context, including security filtering mechanisms. This study proposes a machine learning-based detection model to address these limitations by identifying parameter tampering vulnerabilities through a contextual analysis. The training dataset aggregates real-world vulnerability cases collected from web crawls, public vulnerability databases, and penetration testing reports. The Synthetic Minority Over-sampling Technique (SMOTE) was employed to address the data imbalance during training. Recall was adopted as the primary evaluation metric to prioritize the detection of true vulnerabilities. Comparative analysis showed that the XGBoost model demonstrated superior performance and was selected as the detection model. Validation was performed using web URLs with known parameter tampering vulnerabilities, achieving a detection rate of 73.3%, outperforming existing open-source automated tools. The proposed model enhances vulnerability detection by incorporating semantic representations of parameters and their values using BERT embeddings, enabling the system to learn contextual characteristics beyond the capabilities of pattern-based methods. These findings suggest the potential of the proposed method for scalable, efficient, and automated security diagnostics in large-scale web environments. Full article

This study examines culture-led urban regeneration as a strategy for revitalizing Meezan Chowk, a historically significant yet deteriorating public space in Quetta, Pakistan. Once a central site of social and commercial exchange, the area suffered from infrastructural decline, overcrowding, and the erosion of its architectural identity. The research proposes a design intervention to restore the site’s heritage value while enhancing its functional and social relevance. A qualitative approach is adopted, incorporating surveys, focus group discussions, and site observations to assess user needs and spatial dynamics. A SWOT analysis serves as the analytical framework to identify the site’s internal strengths and weaknesses, as well as external opportunities and threats. By utilizing the Geographic Information Systems (GIS) and OpenStreetMap data, further information can enhance understanding of the site’s urban morphology. The proposed design integrates vernacular elements, such as arched facades, shaded corridors, and communal courtyards, with contemporary features, including cafes, local artisan shops, and accessible public amenities. Full article

Ethnobotanical knowledge and biodiversity are critical components of sustainable natural resource management, especially in regions undergoing rapid environmental and socio-economic change. In Northeast Thailand, traditional plant knowledge is deeply intertwined with local cultural identity but faces increasing threats from urbanization, agricultural expansion, and generational shifts. This study presents a floristic and ethnobotanical survey of the Khanthararat Public Benefit Forest, a community-managed remnant forest in Maha Sarakham Province, documenting 110 plant species from 42 families. The Fabaceae family was the most diverse, consistent with other tropical ecosystems. Predominantly native species (85.45%) indicate minimal disturbance, while introduced (11.82%) and endemic species (2.73%) reflect ecological complexity. Ethnobotanical data revealed 34 wild edible species, 33 medicinal plants, and 19 ornamental species used by the local community, highlighting the forest’s vital role in supporting livelihoods and cultural practices. High Use Values (UVs) for species such as Spondias pinnata and Coccinia grandis underline their dual importance in food and medicine. Informant Consensus Factor (Fic) values demonstrate strong agreement on plant use for reproductive and musculoskeletal health, reflecting well-established traditional knowledge. The findings underscore the forest’s dual significance as an ecological hotspot and a repository of cultural heritage, providing essential ecosystem services including biodiversity conservation, climate regulation, and cultural provisioning. By integrating traditional knowledge with biodiversity assessment, this study offers valuable insights for community-based conservation strategies that sustain both ecological integrity and cultural resilience in Northeast Thailand. Full article

The rapid expansion of IoT in smart cities has improved traffic management but increased security risks. Traditional IDS struggle with advanced threats, prompting adaptive solutions. This work proposes a framework combining machine learning (ML), Zero Trust Architecture (ZTA), and blockchain authentication. Supervised models (XGBoost, RF, SVM, LR) detect known anomalies, while a CNN Autoencoder identifies novel threats. Blockchain ensures identity integrity, and compromised devices are isolated automatically. Tests on the IoT-23 dataset demonstrate superior accuracy, fewer false positives, and better scalability than conventional methods. The integration of AI, Zero Trust, and blockchain significantly boosts IoT traffic system security and resilience. Full article

One of the most significant factors shaping the formation of new urban structures is climate change—including global warming and the associated emerging issues—heatwaves, storms, hurricanes, floods, droughts, fires and others. In recent times, new threats have emerged, including war risks, radiation, pandemics and other potential factors, whose devastating consequences are no less severe than those of climate change. Concerning these and other potential threats, this work aims to develop a new, sustainable urban structure element—a territorial unit or complex to be used in creating a new city planning framework. The formation of this sustainable urban unit or complex is based on three fundamental sustainability principles—social, ecological and economic—the harmonious interaction of which can enable the creation of a safe, healthy and convenient urban environment for living, working and leisure. Such a structural urban complex would consist of a group of neighbourhoods with various building densities, enclosed by public transport streets that integrate the complex into the city’s overall spatial structure. To support the complex’s functioning, a structural element—a green core—is planned at its centre, serving as a space for residents’ recreation, protection from various threats and social interaction. Given that this technical, structural and urban territorial unit, in terms of its autonomous functionality, structure, composition, significance and other characteristics, is identical to a natural cell, it is proposed (based on the principles of bionics) to name this structural urban territorial unit an ‘urban cell’ or ‘urbocell’ for semantic clarity. Full article