Search Results (97)

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

Background: Recent advancements in deep learning have enabled the development of automated software to assist in ischemic lesion detection on diffusion-weighted imaging (DWI), but their real-world performance remains underexplored. This study evaluated the diagnostic performance of a commercially available, CE-marked (MDR class IIa) artificial intelligence (AI) software version 1.0 for detecting ischemic lesions on DWI and examined its sensitivity in relation to lesion-specific characteristics. Methods: A retrospective cohort of 235 patients with confirmed ischemic stroke who underwent DWI was analyzed. The CE-marked software’s performance was assessed at both lesion and patient-level, using expert neurologist interpretations as the reference standard. Lesion characteristics, including maximum axial size, apparent diffusion coefficient (ADC) values, slice coverage, and anatomical location, were analyzed. Results: The software achieved a lesion-level sensitivity of 83.51% (95% CI, 79.8–86.8%) and a patient-level sensitivity of 95.31% (95% CI, 91.8–97.6%). Undetected lesions were significantly smaller, covered fewer slices, and had higher ADC values. No significant differences were observed in detection rates by anatomical locations, vascular territories, or time from symptom onset. Conclusions: While the AI software demonstrated a strong patient-level sensitivity overall, it showed limitations in identifying smaller, less conspicuous lesions. These findings underscore the need to optimize deep learning algorithms for better sensitivity and highlight the importance of clinician awareness regarding AI limitations in acute stroke care. Full article

Historical water infrastructures represent an overlooked cultural heritage of extraordinary importance, encompassing centuries of technical knowledge deeply intertwined with the landscape and social life. Matera stands out as a case study of international relevance, where the morphology of the historic urban fabric of the Sassi has been shaped by the Grabiglioni, or Fossi, streams that today lie hidden and compromised, deprived of the recognition they deserve. This study presents an integrated analysis that combines history, morphology, hydrology, and infrastructure to uncover the origin, evolution and cultural value of the entire context. Thus, the environmental and identity-related potential of these historical infrastructures emerges, along with the critical issues they pose, partly as a consequence of urban expansions. Reintegrating the Grabiglioni into urban development policies is not merely a matter of preservation; it represents a strategic opportunity to transform this heritage into a resource for safety, sustainability, and urban regeneration. The multidisciplinary approach proposed here can serve as a guide for similar studies on historical water infrastructures, restoring life and memory to legacies that narrate a timeless engineering intelligence and a careful understanding of the various territorial components (morphology, climate, works, and transformations). This article is a revised and expanded version of Altamura D. et al., Interdisciplinary investigation approach to analyze historical water infrastructures and urban transformations: the case study of the Grabiglioni in the Sassi of Matera, Italy, presented at CEES—International Conference on Construction, Energy, Environment and Sustainability in Bari (2025). Full article

Corporate team unity and role clarity are crucial for organizational success and human resources. This study examines how job clarity affects employee performance and innovative work behavior (IWB) via organizational citizenship behavior (OCB). Additionally, to determine how artificial intelligence (AI) information and leader-member exchange (LMX) moderate the relationship between job clarity, IWB, and employee performance. This research focused on Pakistan’s Federal Capital Territory (FCT) Islamabad, and Punjab province’s IT sectors. The self-administered questionnaire received data from 555 IT professionals. The suggested model was tested using Smart PLS structural equation modeling. Results showed that job clarity and OCB significantly improve IWB and employee performance. Role clarity, IWB, and employee performance are partly mediated by OCB. In addition, LMX adversely moderates the relationship between job clarity and IWB and employee performance, but not AI information. Emphasis is primarily placed on elucidating the respective roles of the employees in order to ensure that they are aware of the expectations placed upon them. Consequently, they are able to demonstrate task performances that are not stipulated in their job descriptions but directly relate to their performance improvement. The current study reveals that human resources (HR) and management should prioritize job clarity and OCB to boost individual performance and IWB. Full article

Assuming that Artificial Intelligence (AI) is changing the approach to urban planning issues, this study investigates whether changes will start to occur at a theoretical level or if technological innovations will mostly be endured rather than used with full knowledge. The authors observed that technological innovation often occurs without a unifying theoretical framework to provide knowledge and a basis for its adoption. The first use of technology in urban management dates to the late 1950s, and it has recently regained attention within the scientific literature; however, a significant deficiency still exists regarding the definition of a theoretical framework for its use. Focusing on the use of AI in urban and regional planning, this study aims to address this gap by outlining theoretical observations that can guide the integration of AI into new approaches for the management of urban transformations. The enormous impact that the rapid and pervasive spread of AI is having on all human activities necessitates the definition of new educational and disciplinary processes, especially in fields like urban planning, which rely on the high potential of such technology for envisioning future scenarios. It is therefore essential to assume that AI will also modify the management of urban and territorial transformations. This study aims to suggest a framework for scholarly debate on the need to define new historical–disciplinary dimensions by appropriately using AI in the phases of urban planning, avoiding the risk of passively accepting AI’s potential by delegating the development of urban planning tools to artificial reasoning. Building on these premises, this study first provides a thorough and critical literature review regarding the use of AI in urban planning and then proposes a methodological framework. The final section discusses the possibilities and limitations of this approach, thereby contributing to the scientific debate on defining a theoretical framework for the adoption of AI within urban and regional planning processes. Full article

Glocalization plays a vital role in promoting regionally embedded sustainable development by enabling territories to adapt global economic impulses to local capacities, values, and institutional frameworks. This paper develops a framework for the quantitative assessment of economic glocalization at the regional level, focusing on the European Union. Drawing on the conceptual metaphor of “refraction”, glocalization is interpreted as a transformation of global economic impulses as they pass through and interact with localized socio-economic structures. The authors construct a Glocalization Index System comprising three sub-indices: (1) Index of Generation of Globalization Impulses, (2) Index of Resistance to Globalization Impulses, and (3) Index of Transformation of Globalization Impulses. Each sub-index integrates normalized indicators related to regional creativity—conceptualized through the four “I”s: Institutions, Intelligence, Inspiration, and Infrastructure—as well as trade and investment dynamics. The empirical analysis reveals substantial interregional variation in glocalization capacities, with regions of Germany, the Netherlands, Sweden, and Finland ranking among the most prominent generators and transformers of globalization impulses. Strong correlations are observed between the Resistance and Transformation indices, supporting the hypothesis that medium resistance levels contribute most effectively to transformation processes. By integrating both global (exogenous) and local (endogenous) dimensions, the proposed framework not only addresses a gap in economic literature but also offers a tool for guiding policies aimed at sustainable, adaptive, and innovation-driven regional development. Full article

Artificial intelligence (AI) is a cutting-edge and revolutionary technology in computer science that has the potential to completely transform a wide range of disciplines, including the social sciences, the arts, and the humanities. Therefore, since its significance has been recognized in engineering and medicine, history, literature, paleography, and archaeology have recently embraced AI as new opportunities have arisen for preserving ancient manuscripts. Acknowledging the importance of digitizing archival documents, this paper explores the use of advanced technologies during this process, showing how these are employed at each stage and how the unique challenges inherent in past scripts are addressed. Our study is based on Cyril’s Lexicon, a Byzantine-era dictionary of great historical and linguistic significance in Greek territory. Full article

This review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability. Full article

In a European context, facing pressure to digitalize public administration, the integration of artificial intelligence (AI) at the local level remains a deeply uneven and empirically poorly understood process. This study investigates the degree of adoption of artificial intelligence (AI) in local public administrations in Germany, exploring territorial disparities and institutional factors influencing this transition. Based on a national sample of 347 municipalities, this research proposes a composite AI adoption index, built by integrating six relevant indicators (including the use of conversational bots and the automation of internal and decision-making processes). In the simulations, local administration profiles were differentiated according to factors such as IT staff (with a weight of 30%), the degree of urbanization (25%), and participation in digital networks (20%), reflecting significant structural variations between regions. The analysis model used is a multilevel one, which highlights the combined influences of local and regional factors. The results indicate a clear stratification of digital innovation capacity, with significant differences between eastern and western Germany, as well as between urban and rural environments. The study contributes to the specialized literature by developing a replicable analytical tool and provides public policy recommendations for reducing interregional digital divides. Full article

The spatial organization of the genome within the nucleus is a fundamental regulator of gene expression, genome stability, and cell identity. This review addresses the central question of how nuclear genome architecture contributes to disease mechanisms and diagnostics, and how technological advances enable its clinical exploration. We first outline the principles of nuclear genome architecture, including chromosome territories, replication timing, and 3D domains, and their role in gene regulation and disease. We then explore the mechanisms and consequences of chromosomal rearrangements, and how replication dynamics intersect with epigenetic regulation and genome stability. Diagnostic tools are presented in chronological progression, from conventional cytogenetics to high-resolution genomic and single-cell techniques. A dedicated section focuses on cancer cytogenomics and its clinical implications. We further highlight emerging technologies for 3D genome and epigenome profiling and their integration into diagnostic workflows. Finally, we discuss current challenges, such as standardization and cost, and the transformative potential of multi-omics and artificial intelligence for future precision diagnostics. Overall, we provide a comprehensive overview of how cytogenetics and cytogenomics contribute to the understanding and clinical diagnosis of genetic and neoplastic diseases. Full article

Remote drilling technologies play a crucial role in automating both underground and open-pit hard rock mining operations. These technologies enhance efficiency and, most importantly, improve safety in the mining sector. Autonomous drilling rigs can navigate to pre-determined positions and utilize the appropriate parameters to drill boreholes effectively. This article explores various aspects of automation, including the integration of advanced data collection methods that monitor the drilling parameters and facilitate the creation of 3D models of rock hardness. The shift toward machine automation involves transitioning from human-operated machines to systems powered by artificial intelligence, which are capable of making real-time decisions. Navigating underground environments presents unique challenges, as traditional RF-based localization systems often fail in these settings. New solutions, such as constant localization and mapping techniques like SLAM (simultaneous localization and mapping), provide innovative methods for navigating mines, particularly in uncharted territories. The development of robotic exploration rigs equipped with modules that can operate autonomously in hazardous areas has the potential to revolutionize mineral exploration in underground mines. This article also discusses solutions aimed at validating and improving existing methods by optimizing drilling strategies to ensure accuracy, enhance efficiency, and ensure safety. These topics are explored in the context of the Horizon Europe-funded PERSEPHONE project, which seeks to deliver fully autonomous, sensor-integrated robotic systems for deep mineral exploration in challenging underground environments. Full article

The integration of artificial intelligence (AI) technologies is reshaping diverse domains of human activity, including natural resource management, urban and rural planning, agri-food systems, industry, energy, education, and healthcare. However, the impact of AI on the sustainability of local ecosystems remains insufficiently systematized. This highlights the need for a comprehensive review that considers spatial, sectoral, and socio-economic characteristics of regions, as well as interdisciplinary approaches to sustainable development. This study presents a scoping review of 198 peer-reviewed publications published between 2010 and March 2025, focusing on applied cases of AI deployment in local contexts. Special attention is given to the role of AI in monitoring water, forest, and agricultural ecosystems, facilitating the digital transformation of businesses and territories, assessing ecosystem services, managing energy systems, and supporting educational and social sustainability. The review includes case studies from Africa, Asia, Europe, and Latin America, covering a wide range of technologies—from machine learning and digital twins to IoT and large language models. Findings indicate that AI holds significant potential for enhancing the efficiency and adaptability of local systems. Nevertheless, its implementation is accompanied by notable risks, including socio-economic disparities, technological inequality, and institutional limitations. The review concludes by outlining research priorities for the sustainable integration of AI into local ecosystems, emphasizing the importance of cross-sectoral collaboration and scientific support for regional digital transformations. Full article

Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing. Full article

Biosphere Reserves (BRs) represent dynamic spaces where the interdependence between nature and people is actively shaped and preserved. These territories serve as living laboratories for sustainable development, blending conservation efforts with local knowledge and cultural traditions. This paper explores how BRs exemplify the interconnection between ecological resilience and biocultural heritage, demonstrating the value of integrating traditional practices into contemporary sustainability frameworks. Using insights from the Memories of Biosphere Reserves project, which has collected over 370 testimonies from Portugal, Brazil, and São Tomé e Príncipe, this study highlights the role of memory and storytelling in reinforcing socio-ecological resilience and informing participatory conservation governance. By documenting personal experiences, traditional land-use practices, and community perceptions, these testimonies foster empathy, intergenerational learning, and ethical engagement with the environment. They also provide crucial knowledge for environmental stewardship and community-driven sustainability strategies. The article further examines the transformative role of digital technologies, open science, and artificial intelligence in preserving and disseminating biocultural heritage. Through georeferenced digital archives and participatory research, communities safeguard their cultural and ecological heritage, ensuring knowledge transmission across generations. By positioning BRs as models for integrated conservation and development, this paper underscores the importance of interconnected socio-ecological systems in achieving sustainability goals. The findings suggest that valuing and preserving biocultural heritage within BRs not only strengthens community identity and resilience, but also provides actionable pathways for addressing contemporary environmental challenges. Full article

The article investigates a learning setting contextualising the use of artificial intelligence in heritage education, with a particular focus on AI systems utilising text-to-image processes. The setting is the one of a university interdisciplinary seminar in communication in South Tyrol, a border region in the north of Italy shaped by a strong cultural identity. The paper illustrates a didactic experience introducing a highly technical and, for most of the students in the chosen context, challenging topic, such as AI. The teaching addresses a critical approach to AI, such as dataset constraints, sustainability, and authorship, and focuses on text-to-image algorithms and artistic co-creation, namely, the shifting role of the artist from sole creator to initiator/collaborator shaping the AI system’s output. The aim of the paper is to contribute to the debate in heritage education on teaching and learning using AI-based systems. The latter are seen as a potential tool for the engagement of students in understanding heritage and its safeguarding and in the relationship between community, territory, and active participation, as emphasised by both the “UNESCO Convention on Intangible Cultural Heritage” and the “Council of Europe Framework Convention on the Value of Cultural Heritage for Society”. However, the current boundaries of AI, particularly in terms of bias and limitations of datasets, must be addressed and reflected on. Full article