Search Results (2,532)

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

Background: With the vast and fast-growing number of mHealth applications supporting health, disease management and self-care for older people, the usability of these applications has become a critical factor determining their acceptance and usage. In order to develop mHealth applications suitable for the aging population, it is important to investigate the relationship between older people’s experience with mobile technology in the past, their perception of the usability of mHealth applications and their subsequent use of these applications. Objective: This study investigated the impact of the length of mobile usage on the perceived mHealth application usability of older adults, and the impact of mHealth application usability on the mHealth application user satisfaction and frequency of use of older adults. Methods: This study is based on a cross-sectional survey among older individuals in Al-Ahsa, Saudi Arabia. The measurement model consisted of five distinct constructs with fifteen corresponding indicators including efficiency, learnability, memorability, error handling, and user satisfaction. In terms of analysis, this study included reliability and descriptive statistics as well as correlation and regression analysis, as well as simple and bootstrapped mediation analysis, and, finally, confirmatory factor analysis (CFA) and structural equation modeling (SEM). Based on discriminant validity, the findings suggest that four first-order dimensions, efficiency, learnability, memorability, and error handling, constitute second-order usability dimensions. Results: A total of 271 older adults were included in the final analysis. All constructs demonstrated satisfactory reliability and convergent validity, with Cronbach’s alpha values ranging from 0.797 to 0.862, Composite Reliability values ranging from 0.798 to 0.860, and Average Variance Extracted values ranging from 0.568 to 0.673. Structural equation modeling revealed that mobile usage duration significantly influenced usability (β = 0.616, p < 0.001), usability significantly influenced user satisfaction (β = 0.953, p < 0.001), and user satisfaction significantly influenced use frequency (β = 0.193, p = 0.002). The second-order structural model demonstrated excellent fit to the data (χ²/df = 1.824, CFI = 0.972, TLI = 0.966, GFI = 0.940, AGFI = 0.928, RMSEA = 0.055). Conclusions: Usability plays a central role in explaining the satisfaction of older people with mHealth services and their continuous use of applications. Older people’s experience with their smartphones is associated with their perceptions of the usability of mHealth applications. Higher perceived usability of mHealth applications is positively associated with greater user satisfaction and more frequent use of these applications among older adults. The findings are in line with a usability-centered technology acceptance model. Design of mHealth services should be based on user-centered design principles. In addition to other design principles, efficiency, learnability, memorability, error handling and other usability principles should be particularly addressed in order to increase acceptance of mHealth services by older people. Full article

The convergence of artificial intelligence (AI) and organoid technology represents a transformative advance toward precision and predictive medicine. Organoids derived from pluripotent stem cells or patient tissues provide physiologically relevant three-dimensional models that recapitulate key aspects of native organ architecture and function. However, intrinsic biological heterogeneity, high-content imaging outputs, and dynamic spatiotemporal processes pose significant analytical challenges that exceed the capacity of conventional approaches. Recent advances in AI and machine learning enable automated image segmentation, quantitative morphometric profiling, and predictive modeling of organoid growth, differentiation, and therapeutic response, thereby enhancing reproducibility and translational relevance. The integration of multimodal datasets, including imaging, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, has further enabled the development of organoid-based digital twins and in silico disease simulations to optimize personalized therapy. AI-enabled organoid-on-a-chip platforms, cloud-based analytics, and federated learning frameworks are accelerating the emergence of scalable, privacy-preserving, and data-driven biomedical ecosystems. Despite these advances, critical challenges persist, including data standardization, model interpretability, ethical governance, and clinical validation. In contrast to existing reviews that emphasize isolated AI applications, this study proposes a unified translational framework integrating AI-driven image analytics, multi-omics integration, digital twins, and organoid-on-a-chip systems within a precision medicine paradigm. By synthesizing current developments, methodological advances, and emerging trends, this study highlights how AI-powered organoid platforms can bridge experimental biology and clinical decision-making, with broad implications for drug discovery, disease modeling, and regenerative medicine. This review aims to provide a comprehensive overview of artificial intelligence–enabled organoid platforms by integrating advances in image analytics, multi-omics data integration, digital twins, and microphysiological systems, while highlighting their potential applications and future directions in precision medicine, drug discovery, and regenerative healthcare. Full article

Social Innovation (SI) has emerged as a strategic paradigm for addressing systemic challenges in highly uncertain environments. However, its practice still reveals epistemological fragmentation that risks reducing SI to welfare-oriented approaches. This article presents a critical and constructive analysis aimed at mitigating the “methodological myopia” that persists in social impact assessment. Through a systematic literature review and a qualitative case study in the Antofagasta Region (Chile), the article argues that the scientific validity of SI depends on longitudinal, multidimensional, and territorially grounded evaluative frameworks. The study examines the relationship between SI and Artificial Intelligence (AI) as a source of methodological rigor, improving traceability and auditability while supporting the scaling of interventions. In response to techno-utopian forms of determinism, the article proposes an ethical and participatory governance framework based on the Multi-Helix model, integrating academia, the public sector, private enterprise, and civil society in the co-creation of public value. The findings suggest that the institutionalization of SI through AI must move beyond procedural efficiency to foster structural transformation. In Antofagasta, this AI-supported certification architecture is already operational within the Regional Innovation Strategy (ERI) 2022–2028 through the executed FIC-R 2023 project on Social Innovation Certification. The Antofagasta experience is therefore presented as an illustrative case of territorial governance, offering transferable principles for other Global South contexts rather than a directly replicable model. Full article

The Architecture, Engineering and Construction (AEC) industry plays a crucial role in global economic development, but it is also a major contributor to environmental degradation and resource consumption. Despite increasing alignment with the United Nations Sustainable Development Goals (SDGs), the sector remains highly fragmented, with limited integration of Modern Methods of Construction (MMC), Lean Management, Circular Economy and Socio-Digital Enablers frameworks into a unified sustainability model. This review article employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to systematically identify and analyse existing literature and address these research gaps. The PRISMA procedure was conducted through a structured process involving the identification of studies from Scopus and Web of Science databases using predefined keywords related to SDGs and the AEC sector. It was followed by screening and eligibility assessment based on publication type, timeframe (2022–2026), subject relevance, and full-text accessibility, resulting in a final dataset of 42 studies for analysis and then applying bibliometric analysis (Biblioshiny and VOSviewer) to define thematic clusters. The results reveal strong research concentration on SDG 11, SDG 12, and SDG 9, while SDG 2 and SDG 14 remain underexplored within the AEC literature. The findings also highlight that the convergence of MMC, Lean Management, Circular Economy practices, and social and digital technologies increasingly drives sustainable industrialisation. A structured content analysis is further conducted to categorise approaches, barriers, and implementation strategies across the four identified components of industrialisation. Overall, this study contributes a comprehensive and integrated framework for understanding sustainable industrialisation in the AEC sector and provides a structured evidence base to support future research and policy development aligned with the SDGs. Full article

Although the literature on entrepreneurial ecosystems recognizes institutional, economic, technological, and social factors, integrated empirical tests of their interrelationships at the regional level remain limited, particularly in transition economies. This paper analyzes a structural-mechanism model of regional entrepreneurial ecosystems in Serbia using survey data from 401 enterprises in four regions. The study applies partial least squares structural equation modeling (PLS-SEM) to assess the measurement model, structural pathways, indirect effects, and predictive relevance of the proposed model. Additional regional comparisons and official regional GDP indicators are used to contextualize the survey-based findings. The results support the reliability and convergent validity of the reflective constructs. The structural model indicates that economic and social factors are associated with institutional and technological conditions, while institutional, technological, and social factors are associated with innovation development, new product and service development, innovation capacity, entrepreneurial activity, and entrepreneurial motivation. The indirect effects further support the proposed mechanism logic, especially through institutional and technological pathways. Regional comparisons show significant differences across the observed regions, with Southern Serbia recording less favorable perceived ecosystem conditions. The findings suggest that regional entrepreneurial ecosystems should be analyzed as context-dependent systems in which structural conditions and operating mechanisms are connected, but the cross-sectional and perception-based design does not allow definitive causal conclusions. Full article

This article reconceptualizes Velimir Khlebnikov’s aesthetic temporality not as a lyrical motif but as an epistemic procedure through which poetic language tests the legibility of history. Treating his historico-mathematical “constants” and his avant-garde formal inventions as mutually implicative, it isolates a double regime: time as oscillatory recurrence (wave, cyclic return, numerical periodicity) and time as a higher-order extension in which duration is spatialized, traversable, and re-coordinatizable. Against linear models of influence, this study situates Khlebnikov within a thick interdiscursive atmosphere—folkloric chronotopes, Romantic time–space convertibility (Novalis), non-Euclidean geometry (Lobachevsky, Riemann), Minkowski–Einstein relativity, and the Russian vogue for “hyperspace” mediated by Morozov and Ouspensky (with Hinton as prototype)—and argues that these matrices are metabolized via poetico-fantastic transmutation rather than imported as explanatory doctrine. Close readings show how reverse temporality, historical inversion, palindromic and collage logics, and the super-narrative architecture of autonomous “planes” operationalize a hyperspatial present in which distant epochs collide without recourse to technological “time machines.” The article thereby reframes Khlebnikov’s project as a cognitive technology: an attempt to re-engineer the chronotope so that past and future become navigable coordinates inside an expanded textual now. In this model, number and word converge as homologous instruments for forecasting, montage, and speculative world-construction itself. 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

To overcome the deformation and failure of surrounding rock in bottom-driven roadways within thick coal seams, this paper proposes a cooperative support theory for the sides and roof of such roadways in deep thick coal seams, based on existing support theories and technologies. The haulage roadway of the 2201 working face in the Yingpanhao Coal Mine is taken as the engineering prototype. Using the proposed theory, three optimized support schemes are developed. Numerical simulations are conducted to compare the deformation and failure behavior of roadway surrounding rock under the original support scheme and the three optimized schemes. The optimal scheme identified by simulation is then implemented in field engineering. The results show that, relative to the original scheme, roof subsidence is reduced by 51.99 mm, 43.83 mm, and 21.41 mm for Optimized ^{Scheme 1}, ^{Scheme 2} and ^{Scheme 3}, respectively, corresponding to reductions of approximately 39.71%, 33.48%, and 16.35%. Under the three optimized schemes, the convergence of the two sidewalls decreases from 480.21 mm to 157.73 mm, 250.84 mm, and 424.24 mm, i.e., reductions of about 67.15%, 47.76%, and 11.66%, respectively. Under the original support scheme, the vertical stress concentration zone is located approximately 5.4 m from the roadway side. Under the three optimized schemes, this distance is reduced to 3.6 m, 3.8 m, and 4.8 m, respectively. The extent of the plastic zone is also smaller under the optimized schemes than under the original scheme, with ^{Scheme 1} exhibiting the greatest reduction. Based on a comprehensive comparison, Optimized ^{Scheme 1} is selected as the optimal support scheme. In addition, ^{Scheme 1} improves deformation asymmetry, with the left–right sidewall asymmetry index decreasing from 3.34% to 0.06% and the sidewall–roof imbalance index decreasing from 3.67 to 2.00. Field application further confirms that this scheme substantially reduces roof–floor convergence and sidewall convergence, verifying the feasibility of the proposed cooperative support theory and technology for the sides and roof in deep bottom-driven roadways of thick coal seams. Full article

Coronary artery disease (CAD) is increasingly recognized as a thromboinflammatory disorder in which innate immune activation and coagulation are tightly coupled within the plaque microenvironment. Emerging single-cell and spatial technologies have refined this paradigm by demonstrating that these processes are not diffusely distributed but instead concentrated within discrete cellular niches. This narrative review critically evaluates mechanistic and translational studies integrating single-cell RNA sequencing, spatial transcriptomics, and ligand–receptor modeling to characterize cell–cell communication networks driving immunothrombosis in CAD. Converging evidence from single-cell and spatial studies indicates substantial heterogeneity among macrophages, neutrophils, and smooth muscle cells, with functionally distinct subpopulations contributing differentially to inflammation, matrix remodeling, and thrombogenicity. Spatial analyses further demonstrate that procoagulant and inflammatory programs converge in anatomically defined high-risk regions, particularly at the plaque shoulder and sites of endothelial dysfunction. However, whether these transcriptional states represent causal drivers or epiphenomena remains unresolved. Many insights are derived from murine models or dissociated tissues, raising concerns regarding translational relevance and loss of spatial context. Additionally, computational inference of intercellular communication remains indirect and requires functional validation. In conclusion, immunothrombosis in CAD should be interpreted as an emergent property of spatially organized cellular networks rather than a uniform inflammatory state. While these approaches identify candidate therapeutic nodes, their clinical translation and the central challenge is to distinguish causal regulatory nodes from transcriptional correlates generated by high-dimensional profiling. Full article

Advancing age, while a natural trajectory, often leads to several age-related diseases (ARDs) and impacts the quality of life (QOL) of the elderly. The World Health Organization (WHO) predicts that the incidence of ARDs is only going to increase over the next couple of decades. Musculoskeletal (MSK) diseases associated with advancing age are a major global health burden and are closely associated with cellular senescence, inflammaging and immunosenescence. To target these, a clear pathway with well-defined biomarkers that can then be translated into clinical applications is needed. Clearly defined biomarkers will bring us one step closer to dissecting age-related MSK changes, tracking these changes with advancing age, predicting these MSK ARDs and thus providing a platform towards healthy aging, disease-free life in the elderly and longevity. This review outlines our current knowledge in the field, discusses the current knowns and unknowns, provides an overview of the anti-aging strategies, and finally encourages its readers to consider approaches to help converge biomarkers of aging for clinical translation using next-generation technologies. 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

The rapid transformation of urban centers into smart environments introduces complex challenges at the intersection of technological advancement, environmental stewardship, and social justice. This study evaluates Lisbon’s smart city transition by establishing an integrated framework that links digital equity with Environmental, Social, and Governance principles. Employing a convergent qualitative research design, this paper triangulates a comprehensive regulatory policy analysis with primary empirical data gathered from twenty-five semi-structured interviews with municipal officials, academic experts, and residents of marginalized communities. The findings expose critical systemic disparities in digital infrastructure deployment, device affordability, and platform literacy across socio-economic strata, demonstrating how localized digital divides directly impede the execution of urban ESG objectives. While green financing mechanisms offer robust pathways for sustainable energy and transit infrastructure, their equity outcomes remain constrained without mandatory, transparent information disclosure systems that mitigate agency costs. Cultivating urban resilience requires shifting from tokenistic e-governance to genuine citizen empowerment. This study offers a novel theoretical contribution by operationalizing corporate ESG metrics within public urban governance frameworks, providing an empirical roadmap for municipal policymakers globally to balance digital innovation with structural inclusion and environmental accountability in smart city agendas. Full article

The modernization of traditional Chinese medicine (TCM) is significantly impeded by the elusive material basis of its meridian system and by a lack of objective, quantitative diagnostic standards. Recent breakthroughs in photonic technologies and optoelectronic chips offer transformative paradigms to address these systemic bottlenecks. This review systematically evaluates the complete academic and engineering chain of “Photonic TCM,” spanning fundamental mechanisms, optical diagnostics, advanced therapeutics, and core chip-level technologies. Specifically, we analyze how ultra-weak photon emission (UPE), two-photon microscopy, and infrared thermography can objectify meridian dynamics and acupuncture pathways. For clinical translation, laser acupuncture has emerged as a robust, non-invasive modality for managing disorders such as chronic pain and insomnia, supported by cumulative evidence-based data. At the device level, vertical-cavity surface-emitting laser (VCSEL)-based photonic computing chips enable ultrafast herbal medicine recognition, while flexible optoelectronics and lab-on-a-chip systems lay the technical groundwork for wearable neuromodulation. Crucially, this review concludes that the Photonic TCM paradigm is transitioning from isolated clinical validation to integrated engineering implementation. We identify biological tissue scattering and parameter heterogeneities as the primary bottlenecks. To navigate these challenges, we propose that the field’s future should converge toward edge-computing-driven wearable closed-loop systems and multi-dimensional optical big data ecosystems. Ultimately, these technological trajectories will steer TCM from an empirical discipline toward a data-driven, precise, and standardized medical science. Full article

The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the efficiency and reliability of PV/T systems is a key enabler of the transition toward sustainable energy. Accurate forecasting of their thermal performance is therefore essential to maximize renewable energy use and reduce energy losses. A deep learning-based method is proposed in this study for the prediction of the thermal efficiency of an air-based PV/T system. More specifically, temporal deep learning architectures are investigated to exploit the complex nonlinear relationships and temporal dependencies governing the thermal behavior of the PV/T collector. A comprehensive comparative analysis is conducted using four state-of-the-art architectures, namely Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the influence of sequence length is examined through a sensitivity analysis considering forecasting horizons of 1 h, 6 h, 12 h, and 24 h. The models are evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrate that forecasting performance is strongly influenced by the selected temporal horizon. Among the investigated configurations, the 24-h horizon provided the most informative temporal context for thermal efficiency prediction. Under this common forecasting horizon, the LSTM model achieved the highest predictive accuracy, reaching an R² of 0.9952, an RMSE of 0.5975, and an MAE of 0.2364, outperforming the TCN, GRU, and Transformer architectures. The residual error and convergence analyses further highlighted the effectiveness of recurrent neural networks in capturing the thermal dynamics of the investigated PV/T system. By enabling accurate and reliable thermal efficiency forecasting, the proposed framework supports improved energy management, higher energy efficiency, and a stronger integration of renewable energy systems, thus contributing to more sustainable operation of hybrid solar energy technologies. Full article