Search Results (1,178)

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

Artificial intelligence has become constitutive of smart city governance, yet data and algorithmic challenges remain analytically separated in existing scholarship, obscuring their recursive coupling and consequences for the built environment. This review synthesises 82 peer-reviewed studies (2020–2025) drawn from a deduplicated corpus of 876 records, combining PRISMA-guided methodology with VOSviewer and CiteSpace bibliometric mapping. Annual output rose from 78 publications in 2020 to 224 in 2024, with ten leading countries contributing roughly 84% of the corpus. The keyword network organises into five thematic clusters spanning AI technical foundations, data governance, algorithmic governance, sustainability, and built-environment governance; emerging 2023–2025 couplings between digital twin and SDG 11, and between generative AI and SDG 11, mark a shifting research frontier, while the algorithmic governance → SDG 16 linkage constitutes the strongest single ribbon in the synthesis. The study advances a double-helix coupling mechanism specifying directional propagation, reverse modulation, and structural cross-linking between data and algorithmic strands, reframing building energy management, digital-twin operation, and smart infrastructure as governance arrangements whose sustainability legitimacy depends on the simultaneous integrity of both strands. Full article

Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems. As municipalities adopt AI to automate, support, and transform administrative processes, organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions. These growing interconnections create new vulnerabilities that can spread across public service networks, yet evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research. This study develops an integrative conceptual framework that examines the relationship between AI adoption, public sector productivity, systemic risk, and organisational resilience within interconnected sociotechnical systems. Drawing on insights from productivity economics, systems theory, and public governance, the framework positions total factor productivity (TFP) within a broader public value and risk governance perspective. Using the PRISMA 2020 methodology, the study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025, assessing productivity outcomes, methodological quality, effect sizes, and contextual factors relevant to local government and networked public administration. The findings show that productivity gains associated with AI are strongly influenced by organisational readiness, including digital maturity, workforce capabilities, governance quality, and institutional coordination. While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems. The review also highlights that resilience depends on the ability of public organisations to anticipate, absorb, adapt to, and recover from AI-related disruptions while maintaining the continuity and quality of public services. The study contributes to theory by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework. It contributes empirically through a comprehensive synthesis of evidence on AI and public sector productivity and methodologically through the application of transparent PRISMA 2020 review procedures. From a practical perspective, the study offers a conceptual measurement framework and policy guidance for municipal decision makers seeking to improve productivity while strengthening resilience and reducing systemic risks in increasingly interconnected public governance systems. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

Digital resilience has become an increasingly important concept in information systems research due to growing dependence on digital infrastructures, escalating cyber threats, and the emergence of regulatory frameworks that formalize resilience obligations. This study provides a systematic literature review of how digital resilience is conceptualized, operationalized, and aligned with emerging European Union (EU) regulatory frameworks. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and IEEE Xplore databases. Fifty-three peer-reviewed studies published between 2006 and 2026 were analyzed using a structured analytical coding framework capturing conceptual clarity, dimensional structure, methodological maturity, and regulatory alignment. The results reveal significant conceptual fragmentation across the literature. While governance, ICT risk management, incident response, and third-party risk management emerge as recurring resilience dimensions, definitional and structural convergence remains limited. Measurement approaches are dominated by maturity models and qualitative assessment frameworks, with relatively few studies proposing validated indicator-based models. Regulatory alignment with EU frameworks such as the Digital Operational Resilience Act (DORA) and the Network and Information Security Directive (NIS2) remains partial and inconsistent. The study identifies a structural alignment gap between regulatory resilience requirements, conceptual resilience models, and operational measurement approaches, providing a foundation for developing regulator-compatible digital resilience assessment frameworks. Full article

The circular economy (CE) is increasingly recognized as a strategic approach that enables firms to address environmental challenges while enhancing competitiveness and long-term value creation. However, evidence regarding its impact on business performance remains fragmented across sectors, performance dimensions, and organizational contexts. This study presents a systematic literature review conducted in accordance with the PRISMA 2020 guidelines to examine how CE practices influence business performance. The review synthesizes evidence from 79 peer-reviewed publications published between 2015 and 2025. The findings identify five major channels through which CE practices affect business performance: (1) economic, environmental, and social performance, (2) operational and supply chain performance, (3) competitive advantage and strategic positioning, (4) financial and environmental performance, and (5) barriers and performance in SMEs. Across these dimensions, CE practices are frequently associated with improved resource efficiency, cost reduction, innovation capacity, supply chain resilience, and enhanced environmental outcomes, including waste reduction and lower emissions. The review suggests that the performance effects of CE are contingent upon contextual factors such as firm size, ownership structure, industry characteristics, regulatory environment, and digital capabilities. While large firms often benefit from greater resources and organizational capacity, SMEs face significant barriers related to finance, technology, and governance, although these can be mitigated through collaboration networks and digitalization. The study contributes to the Strategic Environmental Management literature by indicating that CE practices may function not only as environmental initiatives but also as strategic capabilities that support competitiveness, resilience, and sustainability transitions. The findings provide implications for managers seeking to integrate circularity into business strategy and for policymakers designing institutional conditions that enable circular business transformation. Full article

Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. Full article

The study aimed to identify the factors influencing the adoption of social media analytics (SMA) for enhanced organizational intellectual capital. It also intended to reveal the challenges linked to the effective incorporation of SMA in organizations for the attainment of enhanced intellectual capital. A systematic literature review (SLR) methodology was applied to address the study’s objectives. The required studies were retrieved from twelve major academic databases (Web of Science, Scopus, ScienceDirect, SpringerLink, Emerald, Wiley Online Library, Taylor & Francis, Sage, INFORMS, SSRN, Dimensions, and Business Source Complete) along with Google Scholar to ensure comprehensive coverage. A total of 40 peer-reviewed journal articles published between 1 January 2012 and 31 December 2025 were selected based on predefined inclusion and exclusion criteria. The findings manifested that factors of human capital, technological infrastructure, social networks, knowledge management, and big data analytics positively influence the adoption of social media analytics (SMA) in organizations for enhanced intellectual capital. It was also identified that data complexity, skills constraints, integration barriers, and ethical concerns negatively affected the incorporation of SMA in organizations. On the basis of the study’s findings, a framework has been developed to efficiently adopt SMA in organizations for enhanced intellectual capital. The framework is universally applicable across all disciplines providing a robust foundation for future empirical validation. The study has provided pertinent theoretical, practical, methodological, and social implications. Full article

This paper investigates the integration of distributed computing and edge Artificial Intelligence (edge AI) as foundational enablers of sixth-generation (6G) mobile networks. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing over 200 peer-reviewed papers, architectural proposals, and standardization documents retrieved from IEEE Xplore, Scopus, Web of Science, MDPI, arXiv, ITU-R, 3GPP, and ETSI, this study provides a structured computational analysis of architectural approaches that integrate distributed computing paradigms and edge AI as core enablers of 6G. The analysis examines the evolution from cloud-centric to edge-centric computing, key edge AI techniques—including Federated Learning (FL), Split Learning (SL), and edge-adapted Large AI Models (LAMs)—and their role in enabling intelligent orchestration, resource optimization, and context-aware services. The comparative analysis demonstrates that edge computing architectures reduce end-to-end latency by 85–95% relative to cloud-centric deployments (under conditions of MEC servers within 1 km and 5G NR fronthaul), while federated learning with gradient compression achieves communication overhead reductions of up to 99% under IID data distributions and stable channel conditions. The results indicate that the tight integration of distributed computing and edge AI enhances network responsiveness, scalability, and adaptability, while also revealing persistent challenges related to orchestration complexity, resource constraints, security, and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realization of 6G networks and for guiding future research and standardization efforts. Full article

Empathy toward animals has received increasing attention because of its relationship to prosocial attitudes, conservation engagement, and environmental concern. Despite growing interest, the way empathy toward animals is measured varies widely across disciplines and applied contexts, making it difficult to compare findings or assess the strength of existing instruments. This paper examines the measurement landscape of empathy toward animals by identifying and describing tools used in both academic research and conservation practice. A search of Web of Science yielded 2155 unique records, resulting in a final sample of 65 peer-reviewed studies with empathy assessment instruments published between 2000 and 2025. These were supplemented by 42 instruments shared by members of the Advancing Conservation through Empathy for Wildlife (ACE for Wildlife^®) Network, one of the largest known networks of professionals focused on enhancing and evaluating empathy toward animals. Across these sources, we observe substantial variation in how empathy is operationalized, including differences in construct emphasis, focal species, intended audiences, and attention to reliability and validity. Academic studies primarily use surveys emphasizing affective empathy toward mammals, whereas practitioner-developed tools are more diverse and often assess cognitive and motivational dimensions across cohort groups. In mapping differences in approaches, we identify persistent gaps and provide suggestions to better align scholarly and applied assessment tools. Full article

Rapid urbanization and increasing landscape fragmentation pose significant threats to ecological connectivity, creating a need for integrative decision support approaches in sustainable spatial planning. This study presents a systematic review of ecological network optimization studies published between 2005 and 2025, following the PRISMA protocol. A total of 78 peer-reviewed studies were analyzed to identify methodological trends, recurring limitations, and research gaps in the assessment of structural and functional connectivity. Based on the gaps identified through the systematic review, this study proposes a conceptual Sustainable Spatial Decision Support System (S-SDSS) framework that integrates Morphological Spatial Pattern Analysis (MSPA), Multi-Criteria Evaluation (MCE/AHP), Minimum Cumulative Resistance (MCR), Least-Cost Path (LCP), and Gravity Modeling (GM) within a unified analytical structure. The review findings reveal a clear shift from single-method applications toward integrated multi-model approaches that better represent ecological processes and improve corridor prioritization. The proposed framework synthesizes the complementary strengths of these established methods to support evidence-based ecological network planning. The framework operates as a hybrid structure that combines a sequential analytical workflow with a unified typological classification system, generating Hybrid Ecological Typologies (T1–T5) as planning-oriented outputs that cannot be produced by any individual method alone. The proposed S-SDSS offers a transferable and policy-relevant conceptual basis for ecological network optimization, supporting green infrastructure planning, biodiversity conservation, and long-term landscape resilience across multiple spatial scales. Full article

This systematic literature review investigates the use of physics-informed neural networks (PINNs) in electromagnetics by examining peer-reviewed articles and conference papers. By integrating governing physical laws into the loss function of a neural network, PINNs offer a mesh-free method in scientific computing. Records published between 2020 and 2025 were retrieved from the databases Scopus, Web of Science, and IEEE Xplore. The initial dataset comprised 500 records, from which 292 unique publications were identified. These were screened, yielding a final set of 139 publications that met predefined eligibility criteria. The analysis reveals growth in research activity, with a pronounced increase from 2022 onward. The literature predominantly addresses electrodynamic problems, employs feedforward neural network architectures, and adopts physics-only training. Two-dimensional problem formulations dominate, with three-dimensional formulations concentrated almost exclusively in electrodynamics, and no publications addressing electroquasistatics were identified. Contingency tables show that methodological choices are not independent of problem characteristics: medium selection correlates with physics regime, and architectural diversity increases with spatial dimensionality. Based on these findings, priorities for future work include: addressing the gap in electroquasistatics, extending three-dimensional formulations to static and quasistatic regimes, broader architectural experimentation in lower-dimensional settings, and increased integration of labeled data in static electromagnetics. To support methodological consistency and reproducibility, a reporting checklist for future PINN-based electromagnetics publications is proposed. Full article

Globally, cis and trans women face increasing rates of HIV, yet the uptake of existing HIV prevention medications often fails to meet their specific needs. This review examines HIV pre-exposure prophylaxis (PrEP) use among cis and trans women, including adolescent girls and young women; newcomers and migrants; sex workers; women who use drugs; and women who have been incarcerated, acknowledging intersectionality exists between these groups. A review of peer-reviewed published literature was conducted, and findings specifically on oral PrEP were synthesized. This review highlights several key themes shaping women’s engagement with PrEP, including barriers to initiation and discontinuation; public health messaging and promotion; the role of women’s networks; intimate partner violence; interpersonal trust in relationships; and “seasons of risk,” where temporary reductions in perceived risk may lead to discontinuation. Additional themes include preferred access points for PrEP, regional differences, and clinical implications for practice. Peer support and peer navigators emerge as important mechanisms for creating safe spaces that enhance trust and sustained PrEP use. Improving PrEP uptake and persistence among women requires a multifaceted, women-centred approach that addresses clinical, social, and structural barriers. Context-specific implementation remains critical to addressing diverse lived realities and strengthening HIV prevention outcomes globally. Full article

Loneliness has emerged as a significant public health concern among vulnerable populations, particularly gay, bisexual, and other men who have sex with men (MSM), and is shaped by sociodemographic and sociocultural factors. This observational, cross-sectional study aimed to estimate the prevalence of loneliness and examine its associations with sociodemographic and sociocultural factors among Brazilian MSM. A total of 1196 participants (mean age = 39.96 years, SD = 12.41) completed measures of loneliness (UCLA Loneliness Scale), sociodemographic characteristics, economic vulnerability, social and community capital, religiosity, and clinical–behavioral factors. More than half of the participants (52.7%) reported moderate or high levels of loneliness. A hierarchical multiple linear regression model was estimated and explained 23% of the variance in loneliness. Greater economic vulnerability and problematic substance use were linked to higher loneliness, whereas being in a romantic relationship, reporting a stronger sense of community belonging, and having social networks composed predominantly of LGBTQIA+ peers were linked to lower loneliness. The absence of formal religion was independently linked to higher loneliness, and HIV serostatus was not significantly related to loneliness after adjustment. These findings highlight the relevance of loneliness in this population and inform interventions targeting material vulnerability and community-based social support. Full article

Key agreement in open networks must not only resist quantum computing threats but also establish keys and authenticate counterparties without explicitly revealing identities. Existing solutions often rely on additional certificates or explicit signature-based authentication or require multiple rounds of interaction and complex state management, thereby imposing burdens on deployment and scalability. To address this, we propose a key agreement protocol based on a post-quantum identity-matching scheme, which unifies identity binding, implicit authentication, and session key establishment into a single key agreement process. Specifically, the initiator generates a ciphertext based on the peer’s identity and embeds session-related information within it, enabling the recipient to verify the peer’s identity and confirm the consistency of the key while decrypting and recovering the shared material. Additionally, bidirectional confirmation messages are used to eliminate negotiation deviations such as the sharing of unknown keys. Furthermore, a version control mechanism is introduced as a synchronization tag for key evolution, allowing the session key to be naturally updated within a predetermined time window. Concurrently, a revocation list maintained by a blockchain is established, enabling distributed verification and auditing of revocation status. This supports key update and revocation management without increasing the number of negotiation rounds. Performance evaluations and security analyses indicate that the protocol incurs a single-end computational overhead of less than 100 μs and a communication overhead of approximately 17.1 KB, trading moderate performance overhead for strong security semantics. Full article