Search Results (106)

The building envelope is a key lever for reducing energy demand and carbon emissions in the built environment. However, conventional envelope systems remain largely static and are unable to respond effectively to changing climatic conditions. Biomimetics has emerged as a promising approach for the development of climate-adaptive envelope solutions. Nevertheless, research in this field remains fragmented across disciplines, and its evolution and technological maturity have not yet been systematically assessed. This study proposes an integrated analytical framework combining a bibliometric analysis of 2.007 Scopus-indexed documents, based on a VOSviewer keyword co-occurrence network, with a cluster-guided state of the art review, and a Technology Readiness Level (TRL) assessment of selected biomimetic envelope solutions. The TRL assessment is conducted using explicit operational criteria. The analysis identifies three main research clusters: (C1) environmental-performative, focusing on energy efficiency and envelope optimisation; (C2) material-experimental, addressing biomimetic composites and innovative materials; and (C3) technological fabrication, centred on digital fabrication, smart materials, and 4D printing. Temporal trends reveal a shift after 2018 from materials science-oriented studies towards computational design and adaptive manufacturing, providing quantitative evidence of a transition previously described mainly in qualitative terms. The review highlights a strong focus on solar-shading applications, while energy harvesting and passive thermoregulation remain comparatively underexplored. The TRL assessment shows that more than 80% of the analysed solutions are concentrated at TRL 3, indicating an early stage of technological development. The main barriers include limited material durability, non-standardised production costs, and regulatory constraints. The findings suggest that future progress will depend less on the identification of new biological inspirations and more on advancing the technological maturity and industrial scalability of existing concepts. This will require integrated developments in materials, parametric design, life-cycle assessment, and regulatory frameworks. Full article

Background: Equitable engagement with technology-mediated learning, including artificial intelligence (AI)-supported pedagogies envisioned in current education policy, depends on material preconditions—school and home connectivity, device access—that are unevenly distributed. Empirical baselines for these preconditions in Central European secondary education remain sparse. This study examines digital infrastructure conditions among Slovak secondary-school students and tests whether connectivity and device access predict curriculum exposure to the smart-city concept, used here as one observable indicator of access to technology-mediated curricular content rather than as a direct measure of AI literacy. Methods: A cross-sectional survey collected data from N = 419 Slovak secondary-school students recruited through the Ministry of Education of the Slovak Republic, regional school authorities, and cooperating secondary schools. Self-rated school internet quality, home internet quality, and total household connected devices were analysed individually and combined into a standardised composite digital readiness index. Curriculum exposure to the smart-city concept (binary: any exposure vs. none/unsure) served as the outcome. Logistic regression was applied in unadjusted and gender-adjusted models (valid n = 383); component-level models tested which infrastructure dimension carried the association. Results: School internet quality was rated low (1–2 on a five-point scale) by 47.6% of students (mean = 2.59), whereas home internet quality was rated high (4–5) by 69.3% (mean = 3.89), indicating a substantial school–home connectivity gap. Only 35.8% of students (148/413) reported any curriculum exposure to the smart-city concept. School internet quality was the principal predictor: each one-standard-deviation (SD) increase corresponded to an odds ratio of 1.564 (95% CI: 1.261–1.939, p < 0.001), and in component-level models, neither home internet (OR = 1.038, p = 0.74) nor household device count (OR = 0.977, p = 0.83) carried independent predictive value. The composite index (OR = 1.337, 95% CI: 1.081–1.654, p = 0.0075 unadjusted; OR = 1.328, p = 0.0095 gender-adjusted) was essentially a noisier reflection of the school-connectivity signal. Male students were significantly more likely to report exposure than female students (OR = 1.794, 95% CI: 1.166–2.762, p = 0.0079). A readiness × gender interaction approached significance (OR = 0.641, p = 0.064), tentatively suggesting a steeper connectivity gradient for female students—an exploratory finding warranting replication. Model discrimination was modest (area under the curve, AUC = 0.621); the association, not predictive performance, is the quantity of interest. Conclusions: School-level connectivity—not home infrastructure or device count—is the infrastructure dimension associated with curriculum exposure to technology-mediated content. These findings indicate that school connectivity should be treated as a precondition for equitable participation in technology-mediated and AI-supported learning, and that pedagogical designs should function under infrastructural constraints. Limitations include reliance on self-rated measures, school-mediated convenience sampling, an observational cross-sectional design, and the indirect mapping between smart-city curriculum exposure and AI literacy proper. Full article

Background: The rapid digitalization of warehouse logistics has accelerated the adoption of smart industrial robots, AI-driven autonomous vehicles, and drones to enhance operational coordination and supply chain performance. However, empirical evidence regarding the combined operational implications of these autonomous technologies within emerging-market logistics environments remains limited. This study examines the relationships between autonomous warehouse technologies and perceived warehouse operational performance in Morocco. Methods: A quantitative cross-sectional research design was employed using survey data collected from 1753 logistics professionals operating in warehouses connected to the SoftLogistics platform. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were conducted to evaluate the proposed relationships and interaction effects among the study constructs. Results: The findings indicate that smart industrial robots, AI-driven autonomous vehicles, and drones are positively associated with perceived warehouse operational efficiency, operational accuracy, and warehouse resilience. The results further suggest that technological interoperability, workforce readiness, organizational adaptation, and AI-enabled coordination mechanisms may enhance the operational contribution of autonomous warehouse systems. Conclusions: Autonomous logistics technologies are associated with smart warehouse transformation and improved operational performance within emerging logistics environments. The findings also highlight the importance of organizational and socio-technical readiness for the implementation of autonomous warehouse technologies. Full article

Periprosthetic joint infection (PJI) remains a major clinical challenge after total joint arthroplasty because of its association with prolonged antimicrobial therapy, repeated surgery, implant failure, functional disability, and substantial socioeconomic burden. Current strategies, including systemic antibiotics, debridement with implant retention, staged revision, and antibiotic-loaded cement spacers, remain indispensable but are limited by mature biofilm tolerance, protected microbial reservoirs, insufficient local drug penetration, persistent inflammation, and compromised periprosthetic bone repair. Increasing evidence indicates that PJI is not merely bacterial colonization of an implant surface, but a dynamic prosthetic interface disorder involving biofilm persistence, immune dysregulation, inflammatory osteolysis, and failed osseointegration. This review summarizes recent advances in anti-infective prosthetic interface design, emphasizing the transition from passive antibacterial coatings toward multifunctional immuno-antibacterial osseointegrative systems. The pathogenic basis of PJI is first discussed, including conditioning film formation, bacterial adhesion, biofilm maturation, protected reservoirs, immune evasion, and osteolysis. Current clinical management limitations are then evaluated, followed by emerging biomaterial strategies, including anti-adhesive and contact-killing surfaces, active antimicrobial coatings, mature biofilm disruption, biological antibiofilm therapies, smart infection-responsive delivery systems, and osteoimmunomodulatory interfaces. Particular attention is given to balancing early antibacterial activity with cytocompatibility, immune resolution, angiogenesis, mechanical durability, and long-term osseointegration. Finally, key translational barriers are highlighted, including load-bearing and tribological constraints, insufficiently standardized mature biofilm and animal models, limited clinical evidence for advanced smart materials, manufacturing reproducibility, sterilization compatibility, regulatory complexity, and application-specific clinical readiness. Future anti-PJI interfaces should evolve beyond unidirectional bacterial killing toward stage-specific systems integrating biofilm control, immune restoration, vascularized bone regeneration, and durable mechanical performance. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

The global transition toward renewable energy has intensified interest in dispatchable low-carbon sources that can support reliability-critical infrastructure in smart grid systems. Data centers represent one of the fastest-growing electricity loads globally, yet their compatibility with biomass-based energy systems as a dispatchable renewable source within smart grid architectures remains poorly understood. This study presented a comprehensive review of biomass power generation, data center energy management, and smart grid integration, drawing on a corpus of 347 peer-reviewed sources. A staged analytical design separated demand characterization from supply evaluation, ensuring that data center energy requirements emerged independently of supply-side assumptions. Using Latent Dirichlet Allocation topic modeling validated with BERTopic and VOSviewer network analysis, the study identified four distinct thematic clusters and found no single topic spanning data center reliability requirements, biomass supply dynamics, and smart grid integration simultaneously, a pattern that points to an underexplored cross-domain space in the literature. A demand–supply–grid alignment framework was introduced to illustrate compatibility conditions across temporal resolution, reliability requirements, and grid management dimensions. The alignment framework and illustrative simulation developed here are offered as analytical starting points to guide future engineering and empirical investigation rather than as demonstrations of operational readiness. An illustrative application demonstrated that biomass feedstock logistics constraints create persistent availability gaps at data center operational timescales, suggesting that supply chain resilience and grid-mediated buffering are likely necessary conditions for viable integration, a proposition that warrants empirical validation through full-scale engineering studies. The findings indicate that integration constraints reflect temporal and operational misalignment rather than technological infeasibility, providing a new analytical perspective for evaluating renewable energy integration in reliability-critical digital infrastructure. Full article

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Low-carbon and smart agriculture talent cultivation requires structural conditions that vary widely across countries. This study develops the Agricultural Talent Cultivation Readiness Index (ATCRI) as a proxy-based structural diagnostic tool for approximating the multi-dimensional enabling conditions and bottlenecks that shape whether SDG-linked agricultural education transformation can be operationalized at scale. ATCRI covers 160 countries across four interdependent dimensions: Education and Research, Digital/Energy/Enabling Infrastructure, Green Transition Pressure, and Innovation/Institutional Capacity. Results indicate a highly uneven global distribution: high transition pressure does not automatically translate into high readiness, with 17 countries exhibiting a pressure–capacity mismatch. China ranks 21st globally, showing a hybrid profile in which education and innovation capacity are strong while digital delivery infrastructure remains a relative bottleneck. Survey evidence from Chinese crop science students is consistent with this interpretation, revealing elevated practice-oriented reform demand where macro-level structural gaps are sharpest. ATCRI is intended as a diagnostic framework for identifying structural bottlenecks, not as a definitive measure of educational quality or reform outcomes. Full article

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

Stakeholders in the tourism system, recognizing the importance of digitalization and the adoption of modern technological solutions, are increasingly integrating artificial intelligence into their operations, including the use of autonomous robots. These initiatives should primarily aim to enhance the customer experience by simplifying and streamlining procedures, while allowing tourism employees to devote more attention to guests. Nautical tourism is a specific form of tourism in which the Republic of Croatia is a global leader; however, it also faces a shortage of qualified staff. Marinas, as the most significant stakeholders within the nautical tourism sector, are the first to invest in the development of innovative solutions. In addition to reviewing the theoretical framework, this paper emphasizes primary research. The aim of the research was to examine the attitudes and habits of nautical tourism guests regarding the adoption of new technologies in marinas, as well as their willingness to use autonomous robots. Given the decision to develop and implement autonomous robots in business operations, the research was conducted among users of nautical services in one of the most modern nautical tourism ports in Croatia and the Mediterranean. A structured online questionnaire was used. The results indicate users’ readiness for the immediate adoption of autonomous robots in certain services, providing a direct incentive for stronger implementation of similar solutions among other stakeholders. This research also suggests that Croatia has the potential to become a technological hub for smart nautical tourism. Full article

Rapid urbanization in Saudi Arabia puts increasing pressure on energy, water, mobility, and waste-management systems, strengthening the need for evidence-based smart-city policy under Vision 2030. Rather than offering a descriptive inventory of projects, this paper develops a verification-oriented framework for assessing smart-city initiatives in the Kingdom. The framework is built on four principles: (i) distinguishing national contextual indicators from city-level evidence, (ii) separating stated ambitions from observed outcomes, (iii) applying an evidence-grading rubric that prioritizes publicly verifiable mechanisms and performance indicators over anecdotal or promotional claims, and (iv) introducing a readiness–impact matrix adapted to Saudi climatic, infrastructural, and institutional conditions. The framework is applied to major Saudi smart-city cases, including NEOM, KAEC, Riyadh, Jeddah, Makkah, and Madinah. The analysis shows that the strongest publicly documented evidence is concentrated in selected sectoral applications, particularly demand response and smart-building control in electricity systems, leak detection and pressure management in water networks, and intelligent traffic management in urban transport. These cases indicate plausible pathways for improving service efficiency and reducing resource waste; however, publicly verifiable city-level outcome data remain limited, fragmented, and uneven across cases. In response, the paper proposes a policy playbook centered on KPI transparency, interoperable data governance, cybersecurity safeguards, and public–private partnership templates to improve the measurability, comparability, and scalability of smart-city outcomes. By formalizing verification and cross-case assessment, the study contributes a reproducible methodological basis for evaluating smart-city progress and prioritizing future investments in Saudi Arabia. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention to environmental sustainability. This paper reconceptualises AI trust mechanisms as socio-technical governance infrastructures that can support both ethical assurance and environmental accountability. Drawing on a comparative qualitative analysis of nine AI trust initiatives, the study develops a three-dimensional analytical framework embedding Environmental Performance Indicators across three governance dimensions: trust-building effectiveness, governance readiness, and sustainable adoption. Applying a systems governance lens, the framework examines how governance instruments structure information flows, institutional practices, and lifecycle feedback relevant to environmental performance. It is analytically illustrated through two urban mobility cases, Helsinki’s Whim application and Barcelona’s smart mobility system, to examine how governance conditions enable or constrain the integration of Environmental Performance Indicators in practice. Findings show that current trust mechanisms lack measurable and publicly visible environmental criteria, indicating a gap between AI assurance and environmental governance. The study contributes a systems-oriented framework for evaluating AI trust mechanisms as governance instruments capable of supporting environmental accountability. While exploratory and based on secondary data, the results indicate that future AI trust mechanisms must incorporate measurable sustainability indicators to support eco-efficient and accountable digital transformation. Full article

The increasing digitalization of smart circular economy (CE) systems intensifies reliance on interconnected cyber-physical infrastructures, thereby increasing exposure to cybersecurity risks that may affect operational continuity and regulatory compliance. This study proposes a Fuzzy Analytical Hierarchy Process (Fuzzy AHP)-based framework to systematically assess cybersecurity readiness in alignment with the ISO/IEC 27001:2022 Information Security Management System (ISMS) standard. The framework adopts a structured three-level hierarchy consisting of seven main criteria and 39 sub-criteria, derived from ISO/IEC 27001:2022 clause-based requirements and Annex A control families, and expanded with an additional regulatory criterion based on the Cyber Resilience Act (CRA) Requirements Standards Mapping. Expert judgments from ten specialists in cybersecurity and digital systems were elicited using linguistic assessments and converted into triangular fuzzy numbers to compute priority weights under uncertainty. The results indicate that ISMS governance and organizational context are the most influential determinants of cybersecurity readiness, followed by regulatory and compliance alignment, operational oversight, and technological controls, while organizational, human, and physical controls play supportive roles. Consistency and sensitivity analyses confirm the robustness and stability of the weighting structure. Overall, the framework provides a standards-aligned decision-support tool for prioritizing cybersecurity readiness in digitally intensive CE environments. Full article

Urban mobility has undergone a significant transformation in recent years, caused by rapid urbanization, environmental pressures, and technological innovation. Even though digital tools and mobility platforms are increasingly used to address transportation challenges, these challenges remain complex and multidimensional, concerning not only infrastructure, but also user behavior, institutional coordination, trust, and social acceptance. Crowdsourcing has proven effective in leveraging distributed knowledge and accelerating innovation in business and public sectors. However, its application in urban mobility contexts has not yet been sufficiently synthesized in a framework-oriented manner. To address this, the study first conducted a comprehensive literature review of existing crowdsourcing assessment frameworks and their applicability to mobility systems. The results show that current implementations in urban mobility often remain fragmented and limited to unidirectional data extraction, lacking comprehensive approaches that integrate technological, social, and organizational dimensions. In response to this, the authors developed the SMART-CROWD framework for assessing cities’ maturity in using crowdsourcing across six dimensions: Strategy & Leadership (S), Methods & Tools (M), Engagement & Representativeness (A), Responsiveness & Impact (R), Technology & Data (T), and Civic Capital & Sustainability (CROWD). Each dimension includes measurable indicators, providing a structured basis of diagnosing disparities between technological capabilities and socio-institutional readiness. The SMART-CROWD framework is intended to support a transition from one-way data acquisition toward more scalable, reciprocal, and citizen-focused innovation ecosystems. This work contributes to the field of applied systems innovation by proposing a structured framework for assessing and guiding the use of distributed intelligence in smart urban mobility. Full article