Search Results (1,384)

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments. Full article

This research evaluates Romania’s alignment with Sustainable Development Goal 9 by examining the relationship between digitalization, innovation capacity, sustainable infrastructure, and industrial environmental performance within the European Union. Using Eurostat data for 2015 and 2023, the research applies hierarchical cluster analysis with Ward’s method and squared Euclidean distance to classify EU Member States according to seven indicators: gross domestic expenditure on R&D, R&D personnel, patent applications to the European Patent Office, sustainable passenger transport, sustainable freight transport, air emission intensity from industry—PM10, and high-speed internet coverage. The analysis identifies five clusters for 2015 and three broader clusters for 2023. The two cross-sectional classifications reveal different patterns of similarity among EU Member States, while substantial structural heterogeneity remains. Leading countries combine strong R&D intensity, high patenting activity, advanced digital infrastructure, and low industrial emission intensity. Romania remains in the structurally constrained cluster in 2023, despite strong high-speed internet coverage and favourable freight-transport performance. The findings show that digital infrastructure alone is insufficient to ensure sustainable industrial growth without stronger innovation capacity, technological output, and cleaner industrial transformation. Full article

The post-COVID-19 tourism recovery exposes a structural divergence between economic resilience and environmental sustainability. Traditional tourism planning metrics consistently fail to diagnose how macroeconomic growth dynamics decouple from environmental pressures, leaving policymakers without empirical tools to identify structural vulnerabilities or prevent carbon-intensive recoupling during post-crisis transitions. This study integrates macroeconomic, environmental, and digital data across a global panel to map actionable pathways for sustainable tourism transitions. Employing a multi-stage methodology, the analysis first utilizes K-Means clustering (n = 80) to isolate the structural fixed effects of baseline destination archetypes driving a K-shaped recovery. Second, using a synchronized environmental panel (n = 41), a Decoupling Index evaluates eco-efficiency elasticity to test the alignment between tourism value recovery and aviation-induced CO₂ emissions. Third, regression analysis of an elite digital cohort (n = 18) measures dynamic exogenous catalysts, revealing that digital attractiveness, proxied by the global digital nomad market share, is a significantly stronger accelerator of recovery (β = 55.59, p = 0.019) than traditional physical air connectivity (β = −46.48, p = 0.036). Synthesizing these insights, a 2 × 2 Strategic Decision Matrix (n = 41) classifies destinations into Sustainable Leaders, Mass-Market Traps, Value Pivoters, and Vulnerable Laggards. The empirical results demonstrate that pre-pandemic structures do not deterministically dictate recovery (p > 0.05, Partial η² ≤ 0.077), yet rapid financial recovery often masks deep atmospheric vulnerabilities, with specific absolute decoupling leaders achieving exceptional value expansion alongside strict carbon contraction (e.g., Saudi Arabia, DE = −0.35; El Salvador, DE = −0.26). This framework provides a data-driven roadmap for policymakers to utilize “soft” digital infrastructure to transition from carbon-intensive, volume-dependent models toward value-optimized, low-emission ecosystems. Full article

This study investigates the relationship between educational leadership, ICT-related factors, and students’ reading performance using the PISA 2022 dataset for Türkiye. Drawing data from 7250 students and 196 school principals, we employed multilevel structural equation modeling and Bayesian estimation. At the student level, between-school actions to sustain learning (SCHSUST) were significantly related to reading achievement. All student-level ICT variables—self-efficacy in digital competencies, practices regarding online information, and subject-related ICT use—significantly predicted reading achievement. Bayesian mediation analysis confirmed significant indirect relationships at student level, indicating that SCHSUST is associated with reading achievement primarily through ICT variables. Students’ economic, social and cultural status predicted reading achievement, indicating socioeconomic inequalities. At the school level, educational leadership (EDULEAD) has been found to be positively associated with preparedness for digital learning and school preparation for remote instruction. However, EDULEAD was negatively related to reading achievement. At the school level, all indirect relationships were insignificant. Furthermore, “economic, social and cultural status” and “academic school selectivity” emerged as the strongest predictors of reading achievement at their respective levels, indicating that the majority of reading inequality is due to substantial between-school inequality, in addition to the socioeconomic basis of digital inequality. Overall, the study highlights that meaningful ICT integration, rather than mere infrastructure provision, is associated with improved reading achievement. Full article

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions. Full article

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system. Full article

Over the past fifty years, urban and rural spaces have been reshaped by global sustainability policies, digital innovation, and emerging socio-ecological needs. This article investigates the convergence of agro-urban planning strategies, Smart City infrastructures, and adaptive governance models, proposing an integrated agro-urban paradigm for sustainable territorial transformation. Drawing on a literature review and comparative analysis of international case studies—including Toronto, Milan, and Woven City—the research develops a triadic interpretative framework based on worldview, program, and faith. The study identifies AgroCities as systems centered on food sovereignty and ecological resilience, Smart Cities as efficiency-driven digital ecosystems, and Adaptive Cities as flexible, human-centered responses to complexity. Findings suggest that integrating food systems, technological innovation, and participatory governance enhances urban resilience and sustainability across scales. The article concludes by advocating for multi-scalar planning tools, cross-sectoral policies, and civic engagement to support the transition toward inclusive and regenerative cities. This framework offers a theoretical and operational contribution to reimagining urban planning in line with the principles of Smart Land and adaptive urbanism. Full article

Achieving balanced development across economic, social, environmental, and agricultural domains remains a critical challenge for emerging economies. This study conducts a comparative assessment of sustainable development in the agro-industrial complex of China, Russia, and India over the period 2000–2023 within an extended SDG-based framework. The methodological approach combines a multi-dimensional indicator system (37 indicators) with the Entropy Weight Method to identify indicators with high temporal information contribution and the Equal Weighting Method to evaluate long-term performance, ensuring both sensitivity to structural changes and cross-country comparability. The results reveal differentiated development trajectories: China demonstrates steady and balanced growth across all dimensions; India shows consistent improvement driven by progress in social and infrastructure-related indicators; Russia exhibits a more volatile pattern with relatively strong social outcomes but persistent weaknesses in agricultural performance. The entropy-based analysis indicates that the indicators contributing most strongly to temporal differentiation vary significantly across countries, with infrastructure and energy transition prevailing in China, natural resource dynamics in Russia, and social and digital factors in India. These findings suggest that long-term development trajectories in the agro-industrial sector are associated with different configurations of resource interdependence, institutional capacity, and resource-use efficiency. Full article

Industrial modernization is widely regarded as an important pathway toward high-quality and sustainable economic development. Using panel data from 30 provinces in mainland China from 2012 to 2023, this study examines the relationship between artificial intelligence (AI) and industrial modernization. AI is proxied by industrial robot density, while industrial modernization is evaluated using a composite index covering supportiveness, substantiveness, innovativeness, greenness, openness, and integration. Fixed-effects models are employed, alongside a series of robustness tests and instrumental variable estimation. The results indicate that AI, as captured by industrial robot density, is positively associated with industrial modernization. This relationship remains robust after adopting alternative measures, introducing lagged explanatory variables and additional controls, applying winsorization, adjusting the sample, and addressing potential endogeneity. Heterogeneity analysis shows that the association is stronger in eastern provinces and in regions with higher levels of AI infrastructure and technical talent. Mechanism analysis suggests that green technology innovation is an important channel through which AI is associated with industrial modernization. In addition, software industry development strengthens the positive association between AI and industrial modernization, highlighting the importance of complementary digital capabilities in supporting industrial transformation. These findings contribute to understanding how AI adoption, represented by industrial robot deployment, is related to industrial modernization and suggest that policies promoting AI-driven industrial transformation should be accompanied by investments in green innovation, software industry development, digital infrastructure, and technical talent cultivation. Full article

Objectives: Inadequate access to safe water, sanitation, and hygiene (WASH) continues to drive infectious diseases, malnutrition, and educational disparities, particularly among vulnerable populations. This systematic review examined WASH intervention strategies implemented in Malaysia between 2014 and 2025, focusing on shifts in hygiene-related knowledge, attitudes and practices (KAP), health outcomes, infrastructure improvements, and implementation challenges. Methods: A comprehensive search across five databases (Science Direct, PubMed, Scopus, Web of Science, and Google Scholar) identified twelve eligible studies targeting schools, healthcare settings, and rural or Indigenous communities. Results: Education-based interventions predominated (n = 10), often employing participatory and theory-driven approaches grounded in the Health Belief Model or Information–Motivation–Behavioural Skills framework. Evidence revealed significant improvements in KAP, particularly when digital, gamified, or storytelling elements were integrated. Community-led and caregiver-inclusive models demonstrated greater behavioural adoption and retention. Thematic analysis identified several implementation challenges, which include (i) sole reliance on self-reported outcomes with limited use of objective indicators, (ii) short intervention durations (<2 months) that limit long-term impact, and (iii) lack of policy and curriculum integration. Conclusions: Findings underscore the need for culturally tailored, longitudinal, and system-embedded interventions that combine behavioural theory with infrastructure investment. Integrating WASH initiatives into Malaysia’s health and education frameworks could advance Sustainable Development Goal 6, ensuring scalable and equitable improvements in hygiene literacy, community resilience, and public health outcomes. Full article

Effectively harnessing the productive potential of local government debt while mitigating financial risks is critical for urban sustainable development. Focusing on the standardized development phase of local government debt in China, this study examines how local government debt affects green technological innovation using Chinese A-share listed firms from 2015 to 2021. We find that regulated explicit debt significantly promotes firms’ green innovation through three channels: green bond issuance, digital infrastructure investment, and improved firm profitability. In contrast, implicit debt exerts a nonlinear inhibitory effect, with significant negative impacts only after a threshold is exceeded. Heterogeneity analysis shows stronger effects in developed regions, areas with more optimized industrial structures, and state-owned, large, and high-ESG firms. Our findings suggest that optimizing debt structure and directing funds to productive green and digital sectors can effectively drive urban green innovation. Full article

Background/Objectives: Atrial fibrillation (AF) imposes a substantial long-term clinical and healthcare system burden, recurrent hospitalizations, impaired quality of life, and increasing long-term healthcare costs. Although patient-reported outcome measures (PROMs) are increasingly used in AF research and clinical practice, their broader role in healthcare delivery, implementation, and system-level decision-making remains insufficiently defined. Existing assessment strategies frequently prioritize symptom burden while underrepresenting cognitive, emotional, social, and functional dimensions of AF-related impairment. This narrative implementation review examines the current role of PROMs in AF management from a healthcare system and implementation perspective. Methods: Literature addressing AF-specific and generic PROM instruments, implementation strategies, health system integration, value-based care, and digital health approaches was reviewed and synthesized across PubMed, Scopus, and Google Scholar. Particular emphasis was placed on implementation barriers, workflow integration, evidence strength, and challenges encountered across diverse healthcare settings. Results: Current PROM frameworks incompletely capture several important dimensions of AF burden, including cognitive dysfunction, sleep disturbance, emotional distress, social participation, sexual health, and productivity loss. Beyond conventional symptom assessment, PROMs may support longitudinal patient monitoring, treatment evaluation, shared decision-making, and patient-centred care. Emerging evidence also suggests potential roles in outpatient prioritization, healthcare quality assessment, and value-based healthcare initiatives, although prospective AF-specific implementation studies remain limited. Mapping PROM applications to the 2024 ESC AF-CARE pathway demonstrates the strongest alignment with the Evaluation and Reducing symptoms domains while supporting patient engagement, comorbidity management, and individualized care planning. Implementation remains constrained by clinician workload, questionnaire fatigue, limited interoperability, heterogeneous digital infrastructure, and variability in organizational resources, with these challenges potentially being more pronounced in smaller or resource-limited healthcare systems. Conclusions: PROM integration in AF care may provide opportunities to strengthen patient-centered management and improve healthcare system responsiveness beyond conventional rhythm- and symptom-focused approaches. Successful implementation may require careful adaptation to local healthcare infrastructure, workflow feasibility, and long-term sustainability. Future developments involving digital platforms, wearable technologies, and artificial intelligence-assisted interpretation may further expand the clinical and operational relevance of PROM-guided AF care. Full article

Artificial intelligence (AI), building information modeling (BIM), and digital twins are increasingly transforming construction sites into smart, data-driven environments that support safer, more efficient, and more sustainable building and urban infrastructure delivery. However, site-level spatial decision-making related to site layout optimization (SLO) remains constrained by fragmented data environments, limited interoperability, and weak integration between planning, monitoring, and adaptive decision-making. This study presents a systematic literature review of how AI, BIM, and enabling digital technologies are being applied to support smart construction site management, site-level spatial decision-making, and SLO-related applications. A Scopus-based search conducted in October 2025 identified 169 records, of which 63 studies were retained following PRISMA-guided screening. Because explicit SLO studies remain limited, the review synthesizes both directly relevant SLO studies and contextually relevant enabling studies with clear implications for smart and sustainable construction operations. The review combines bibliometric analysis, thematic content analysis, and cross-functional technology mapping to examine the intellectual structure of the field, the main operational domains addressed, and the dominant technological convergences supporting intelligent site decision-making. The findings show that the field is expanding rapidly but remains unevenly consolidated, with greater evidence concentration and practical readiness in real-time digital twin and spatial data management, automated monitoring, and proactive safety intelligence than in closed-loop logistics coordination and autonomous mobility. Across application domains, the dominant technology convergences combine machine learning and deep learning with multidimensional BIM, frequently extended through digital twins, sensors, cloud platforms, UAVs, simulation tools, and GIS-related infrastructures. The review further shows that the main barriers to deployment are not merely algorithmic, but also relate to interoperability, data quality, implementation complexity, human oversight, and limited field validation. Overall, this study provides a structured synthesis of evidence concentration, practical readiness, dominant patterns, and unresolved gaps of AI-BIM-enabled smart construction site management, and outlines directions for more interoperable, human-centered, and field-validated systems that support sustainable smart building and urban infrastructure delivery. Full article

Digital twin (DT) technology is reshaping structural engineering by linking physical assets to dynamic and data-driven virtual counterparts. DTs enable monitoring, predictive analytics, and autonomous decisions across design, construction, operation, and maintenance. Additionally, DTs are updated with real-time streams continuously. This study focuses on the applications of DTs and the intersection between the Internet of Things (IoT), Building Information Modeling (BIM), and artificial intelligence (AI). Applications include structural health monitoring (SHM) and predictive maintenance for bridges and buildings, in addition to construction safety optimization and stewardship of architectural heritage. The paper also examines barriers to adoption, including data interoperability, cybersecurity, upfront cost, and workforce readiness, and discusses standardization needs. In addition, it highlights educational impacts and pathways for small and medium enterprises (SMEs) to adopt scalable DT solutions. By consolidating recent advances, the review shows how DTs can deliver more resilient, efficient, sustainable, and intelligent infrastructure and outlines the research priorities to overcome remaining gaps and fully realize their potential. Full article

Industrial digitalization is moving from Industry 4.0 toward Industry 5.0’s emphasis on resilience, human-centric operation, and sustainability. This shift is enabled by the convergence of Operational Technology and Information Technology, but this integration also broadens the exposure of industrial infrastructures to cyber threats targeting communication integrity and process continuity. Mitigating these risks requires network control that is both programmable and aware of each asset’s operational context. However, there is still a lack of operational interfaces that translate the semantics of industrial assets into programmable, runtime-enforceable network behavior. In this paper, following a Design Science Research methodology, we introduce an asset-aware, closed-loop network control abstraction in which the industrial network itself is modeled as a managed asset through Asset Administration Shells. Asset state, lifecycle phase, and operational intent are translated into network policies enforced at runtime on programmable data planes, while in-network telemetry is exposed at the asset level and correlated with operational metrics. We validate the abstraction on a hybrid testbed that combines virtualized components with industrial-grade hardware and virtualized 5G connectivity, through three security-oriented use cases: (i) asset-driven customization of forwarding policies; (ii) human-centric secure maintenance with controlled remote access over 5G; and (iii) anomaly detection and isolation based on cross-layer telemetry correlation. The results show that asset-level operations can drive programmable network enforcement and make network telemetry available at the asset layer. Finally, the work outlines a first step toward standardizing network-oriented asset submodels by separating control-plane operations from data-plane state and telemetry. Full article