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As artificial intelligence (AI) becomes increasingly embedded in organizational processes, questions about its implications for leadership have gained growing relevance. However, the existing literature remains fragmented, often addressing strategy, leadership capabilities, governance structures, or ethical concerns in isolation, without explaining how these dimensions interact to shape leadership effectiveness in AI-driven environments. This study conducts a PRISMA-guided systematic review of 33 peer-reviewed articles to examine how AI-embedded leadership is conceptualized across contexts. By synthesizing findings across strategic, human, and governance domains, the analysis identifies recurring patterns and structural relationships in the literature. The results indicate that effective leadership in AI-intensive settings is not determined solely by technological adoption or digital competencies, but by the alignment between the depth of AI integration in decision-making processes, leaders’ capacity to interpret and oversee algorithmic outputs, and the presence of governance mechanisms that ensure transparency, accountability, and trust. While some studies highlight potential opportunities associated with AI, these remain less systematically developed compared to the extensive focus on challenges and emerging risks. On this basis, the study introduces the AI-Leadership Configurational Framework (ALCF), a multi-level model that conceptualizes leadership effectiveness as the outcome of systemic alignment. The framework integrates previously disconnected debates and provides a coherent foundation for future empirical research on leadership in the algorithmic age. Full article

(This article belongs to the Special Issue Impact of AI on Business Sustainability and Efficiency)

12 pages, 227 KB

Open AccessReview

Neurosurgery Advancements: From Technical Innovation to Patient-Centered Outcomes—A Narrative Review

by Vianney Gilard

J. Clin. Med. 2026, 15(8), 3140; https://doi.org/10.3390/jcm15083140 (registering DOI) - 20 Apr 2026

Abstract

Over the past decades, neurosurgery has undergone a profound transformation driven by technological innovation and a paradigm shift toward patient-centered outcomes. Historically evaluated through mortality rates and extent of resection, modern neurosurgery increasingly prioritizes preservation of neurological function, cognitive integrity, and quality of life. Innovations such as intraoperative mapping, multimodal neuromonitoring, image-guided surgery, minimally invasive techniques, and enhanced recovery protocols have reshaped surgical decision-making. However, the true value of these advancements lies in their measurable impact on clinically meaningful outcomes. This narrative review examines how technical progress translates into functional, cognitive, and quality-of-life benefits, while critically discussing current limitations of evidence and future directions toward personalized, outcome-driven neurosurgery. Full article

(This article belongs to the Special Issue Neurosurgery Advancements: Techniques and Patient Outcomes)

15 pages, 264 KB

Open AccessArticle

Medical Practitioners’ Acceptance and Use of AI-Based Clinical Decision Support Systems in Western China: A Mixed-Methods Study

by Runping Zhu, Zunbin Huo, Yue Li, Banlinxin Gao and Richard Krever

Healthcare 2026, 14(8), 1096; https://doi.org/10.3390/healthcare14081096 (registering DOI) - 20 Apr 2026

Abstract

Background: Doctors have made increasing use of artificial intelligence-based clinical decision support systems in recent years in eastern China, but far less so in poorer western China, where hospitals with less access to specialized expert services might be expected to make greater use of such aids. Methods: This study of the reasons for lower uptake in the western hospitals focused on a tertiary referral hospital in the capital city of the poorest province in China. Drawing on UTAUT (unified theory of acceptance and use of technology) theoretical literature and previous studies, seven variables most likely to explain the limited adoption of the technology were identified and tested by means of an explanatory sequential mixed-methods study. Results: Initial bivariate tests revealed no significant differences across variables; however, multivariate logistic regression identified social influence as the sole statistically significant predictor of adoption willingness. Follow-up structured interviews revealed a surprisingly low awareness of the technology by medical personnel, with very limited deployment. Conclusions: The failure to adopt AI diagnosis technology is attributable not to the variables usually cited as factors inhibiting technology adoption but rather the failure of hospital and medical faculty administrators to acquire the technology and train doctors and medical students. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)

22 pages, 3673 KB

Open AccessArticle

A Novel Gradient-Based Method for Decision Trees Optimizing Arbitrary Differential Loss Functions

by Andrei Konstantinov, Lev Utkin and Vladimir Muliukha

Mathematics 2026, 14(8), 1379; https://doi.org/10.3390/math14081379 (registering DOI) - 20 Apr 2026

Abstract

There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method’s applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 3rd Edition)

41 pages, 1216 KB

Open AccessArticle

Scaffolding Generative AI as a Tutor: A Quasi-Experimental Study of Learning Outcomes and Motivational, Cognitive and Metacognitive Processes

by Chrysanthi Melanou and Maik Beege

Educ. Sci. 2026, 16(4), 651; https://doi.org/10.3390/educsci16040651 (registering DOI) - 20 Apr 2026

Abstract

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

(This article belongs to the Topic Generative Artificial Intelligence in Higher Education)

28 pages, 1168 KB

Open AccessArticle

Climate Change in Built Environment: Remote Sensing for Thermal Assessment Measurement Paradigms

by Maria Michaela Pani, Stefano Urbinati, Chiara Mastellari, Lorenzo Mariani and Fabrizio Tucci

Appl. Sci. 2026, 16(8), 3992; https://doi.org/10.3390/app16083992 (registering DOI) - 20 Apr 2026

Abstract

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

(This article belongs to the Special Issue The Rome Technopole Project: Energy Transition and Digital Transition in Urban Regeneration and Construction)

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22 pages, 3182 KB

Open AccessArticle

Modeling and Dynamic Analysis of Trust Decay in Social Media Based on Triadic Closure Structure

by Yao Qu, Changjing Wang and Qi Tian

Entropy 2026, 28(4), 468; https://doi.org/10.3390/e28040468 (registering DOI) - 20 Apr 2026

Abstract

Trust decay in social media is a serious threat to user experience and platform ecology. To solve this problem, this paper focuses on triadic closure in the infrastructure of social networks and explores its mechanism in trust decay prevention. Based on the systematic comparison of the ER random graph, the BA scale-free network, a forest fire model, and complete graph approaches, two core metrics, the trust decay risk index and trust resilience index, are proposed in this paper. Combined with structural indices such as the clustering coefficient, the average path length, and the triangular closure number and its growth rate, the quantitative relationship between network structure evolution and trust decay risk is established. It is found that the forest fire model exhibits optimal trust resilience in structure due to its power-law growth characteristics of high clustering, short path length and triangular closure; the dynamic mechanism of trust decay under different network growth modes is significantly different. The validity of the theoretical framework is further supported by the verification of Sina Weibo attention relationship network data. The analysis framework of network growth evolution based on fusion triangle closure and the risk and resilience indicators defined in this paper provides a computable theoretical tool for understanding and predicting trust evolution in social media from the perspective of network structure. Full article

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24 pages, 4858 KB

Open AccessArticle

Reconstructing Shallow River Bathymetry Through Sequence-Based Modeling Approach

by Modestas Butnorius, Timas Akelis, Matas Vaitkevičius, Dominykas Matulis, Andrius Kriščiūnas, Vytautas Akstinas and Rimantas Barauskas

Water 2026, 18(8), 975; https://doi.org/10.3390/w18080975 (registering DOI) - 20 Apr 2026

Abstract

Hydrological monitoring is crucial for protecting aquatic ecosystems, especially downstream of hydropower plants where water levels can change suddenly and cause the degradation of instream habitats. There are lot of traditional methods used to monitor water levels and river bathymetry, but most of them rely on in situ measurements. Drone-based remote sensing has received more attention in recent years, with the data in turn processed using CNNs. In this paper, we propose a new sequence-based method that uses multiple frames to expand the available context and compare it to already existing methods, such as Lyzenga, Stumpf, CNN, and SfM. The best performing models within this study end up being SfM and CNN, with the former being more accurate on rivers with clean riverbeds and the latter being the most consistent. The sequence-based model shows promise, and even outperforms CNN, in terms of MAE, on rivers where the same location across multiple views is mapped, achieving the most accurate results across different images. This shows that utilizing multiple views to increase the available context can improve the accuracy of riverine depth estimation based on multispectral visual information. Full article

(This article belongs to the Special Issue Integrating AI and Remote Sensing for Ecohydrological Monitoring and Prediction)

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8 pages, 358 KB

Open AccessProceeding Paper

Air Traffic Demand Forecasting for Origin–Destination Airport Pairs Using Artificial Intelligence

by Alicia Serrano Ortega, Albert Ruiz Martín and Clara Argerich Martín

Eng. Proc. 2026, 133(1), 25; https://doi.org/10.3390/engproc2026133025 (registering DOI) - 20 Apr 2026

Abstract

The accurate anticipation of passenger demand across specific origin–destination (OD) airport routes is a cornerstone of strategic and operational decision-making within the global aviation sector, including airlines optimizing fleet and route management, airports planning infrastructure development, and regulatory bodies overseeing airspace efficiency. However, conventional forecasting techniques frequently encounter limitations when confronted with the inherent complexities and non-linear interdependencies that characterize air travel demand patterns. These patterns are shaped by an array of dynamic variables, including macroeconomic trends, population dynamics, distinct seasonal variations, and emergent phenomena. This investigation evaluates the utility of Artificial Intelligence (AI) paradigms in constructing predictive models for monthly passenger volumes between international OD airport pairs. This work highlights the ongoing transformative impact of AI methodologies on forecasting tasks within the aviation industry. Full article

(This article belongs to the Proceedings of The 15th EASN International Conference on “Innovation in Aviation & Space Towards Sustainability Today & Tomorrow”)

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15 pages, 3994 KB

Open AccessArticle

Three-Dimensional Shape Measurement Using Speckle-Assisted Phase-Order Lines Without Phase Unwrapping

by Ziyou Zhang and Weipeng Yang

Sensors 2026, 26(8), 2534; https://doi.org/10.3390/s26082534 (registering DOI) - 20 Apr 2026

Abstract

Achieving high-accuracy and high-speed 3D shape measurement remains a significant challenge. This paper presents a novel technique using phase-order lines (POLs), which eliminates the need for phase unwrapping in a binocular system. By combining phase-shifting for high resolution and speckle projection for robust features, our method extracts POLs directly from the wrapped phase. The speckle patterns are then used to establish robust POL correspondences between stereo images. These matched POLs serve as reliable seeds to guide dense, sub-pixel matching directly on the wrapped phase, thus bypassing the complex phase unwrapping process. This approach significantly reduces the number of required patterns. The experimental results demonstrate that our method achieves a root-mean-square (RMS) error of 0.058 mm using only five patterns, delivering accuracy comparable to a 12-pattern temporal phase unwrapping (TPU) method while being significantly faster. Full article

(This article belongs to the Collection 3D/4D Optical Imaging Sensors for Surface Measurement, Processing and Applications)

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17 pages, 8176 KB

Open AccessArticle

A Multi Scenario Simulation Study on the Systemic Benefits of Fleet Electrification for Urban Sustainability in Shanghai

by Wanxing Sheng, Keyan Liu, Dongli Jia, Jun Zhou, Zezhou Wang, Chenbo Wang, Xiang Li and Yuting Feng

Sustainability 2026, 18(8), 4077; https://doi.org/10.3390/su18084077 (registering DOI) - 20 Apr 2026

Abstract

Fleet electrification is increasingly recognized as a cornerstone of urban decarbonization in high-density megacities. This study introduces a multi-scenario simulation framework integrating high-resolution mobile signaling data with traffic modeling to quantify the systemic environmental and energy impacts of road-based battery electric vehicle (BEV) integration in Shanghai. By evaluating both a fixed-fleet baseline and dynamic-fleet growth scenarios focused on the urban road network, we find that aggressive fleet electrification leads to a profound reduction in aggregate carbon emissions and criteria pollutants, effectively decoupling transit-related environmental burdens from urban growth. However, results also highlight a significant energy trade-off: while fossil fuel displacement accelerates, grid-based electricity demand increases under fleet growth conditions. Within this context, the expanded vehicle population exacerbates urban congestion, which disproportionately inflates the fuel consumption of remaining internal combustion vehicles. Their operational efficiency is severely compromised by frequent stop-and-go cycles, leading to an intensification of idling losses. Ultimately, this research highlights the capability of the proposed simulation framework to provide granular insights into urban emission dynamics, offering a quantitative foundation for policymakers to harmonize electrification targets with proactive traffic management and grid infrastructure strengthening to evaluate the systemic trade-offs toward achieving long-term urban sustainability. Full article

(This article belongs to the Section Sustainable Transportation)

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14 pages, 276 KB

Open AccessArticle

Layered Control Architectures for AI Safety: A Cybersecurity-Oriented Systems Framework

by Young B. Choi, Paul C. Hong and Young Soo Park

Systems 2026, 14(4), 447; https://doi.org/10.3390/systems14040447 (registering DOI) - 20 Apr 2026

Abstract

As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts. Full article

(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)

18 pages, 349 KB

Open AccessReview

Autoimmune Hepatitis: Emerging Frontiers in Research and Clinical Management

by Armando Curto, Irene Scami, Giulia Gliottone, Rocco G. Iamello, Erica N. Lynch and Andrea Galli

Gastrointest. Disord. 2026, 8(2), 20; https://doi.org/10.3390/gidisord8020020 (registering DOI) - 20 Apr 2026

Abstract

Autoimmune hepatitis (AIH) is a chronic immune-mediated liver disorder that, without treatment, can advance to fibrosis and cirrhosis. Although standard regimens with corticosteroids and thiopurines have significantly improved survival, many patients still experience relapses and drug-related toxicity, highlighting the urgent need for alternative strategies. Recent studies underscore AIH’s multifactorial nature, revealing intricate interactions among genetic susceptibility, environmental triggers, and dysregulated immune responses. Next-generation diagnostics, ranging from novel biomarkers to high-resolution imaging, are enhancing early detection and more precise disease classification. At the same time, multi-omics analyses and artificial-intelligence-based models are refining predictions of disease trajectory and therapeutic response. On the treatment horizon, investigational options such as targeted immunomodulators, B-cell–depleting therapies, and cell-based interventions aim to achieve durable remission while minimizing adverse effects. This review critically appraises these advances and explores how integrating epidemiological insights with cutting-edge research in pathogenesis, diagnostics, and therapy could pave the way for more personalized and effective management of AIH. Full article

(This article belongs to the Special Issue Feature Papers in Gastrointestinal Disorders in 2025–2026)

15 pages, 234 KB

Open AccessArticle

Enhancing or Jeopardizing Human Creativity? Will Humans Be Able to Defend Themselves Against AI Superpowers in an Age of Ethics Washing and Law Washing?

by Lorenzo Magnani

Philosophies 2026, 11(2), 65; https://doi.org/10.3390/philosophies11020065 (registering DOI) - 20 Apr 2026

Abstract

I recently introduced the concept of eco-cognitive openness and situatedness to explain how cognitive systems—human or artificial—dynamically interact with their environments to generate information and creative outputs through abductive cognition. Humans display high eco-cognitive openness, integrating tools and cultural contexts through “unlocked strategies” that also enable exceptional creativity. By contrast, generative AI like LLMs operates via “locked strategies” based on pre-existing datasets with limited real-time interaction, which constrains higher creativity. Although LLMs surpass humans in many cognitive tasks, they lack the openness required for truly advanced abductive performance. Notably, most human cognition is repetitive and imitative—humans themselves often resemble “stochastic parrots.” In this sense, LLMs reveal human intellectual poverty more than they expose flaws in artificial intelligence. I will illustrate how LLMs can act as powerful enhancers of human performance while simultaneously threatening our most distinctive prerogative: creativity. Future human–AI collaboration could expand our eco-cognitive openness, but demands vigilant oversight to counter bias and so-called overcomputationalization. GenAI can serve as an epistemic mediator toward unlocked creativity only if humans maintain agency and embed its outputs in broader socio-cultural frameworks. My greatest concern is that ethical and legal safeguards will remain ineffective in practice, resulting in mere “ethics washing” and “law washing” without genuine enforcement. Full article

(This article belongs to the Special Issue Intelligent Inquiry into Intelligence)

25 pages, 1520 KB

Open AccessReview

Resveratrol and Redox Regulation in Cardiovascular Disease Across the Life Course: Mechanistic and Translational Perspectives

by Chien-Ning Hsu and You-Lin Tain

Antioxidants 2026, 15(4), 509; https://doi.org/10.3390/antiox15040509 - 20 Apr 2026

Abstract

Resveratrol (RSV), a bioactive polyphenol, has emerged as a pleiotropic modulator within the integrated pathophysiology of cardiovascular disease (CVD) across the life course. Effective CVD management requires a transition from organ-centric frameworks to systems-level models that acknowledge dynamic crosstalk among metabolic, renal, and cardiovascular networks. Oxidative stress constitutes a central unifying axis in this interconnected biology, propagating cross-organ injury from early developmental stages onward. Mechanistically, RSV acts as a redox-responsive gene regulator by activating the Nrf2–ARE pathway, restoring nitric oxide bioavailability, and orchestrating SIRT1, AMPK, and NF-κB signaling to recalibrate mitochondrial function, inflammatory tone, and endothelial integrity. Within the Developmental Origins of Health and Disease (DOHaD) paradigm, RSV exhibits reprogramming potential that attenuates the intergenerational transmission of hypertension, kidney disease, and metabolic dysfunction. Although clinical translation is constrained by limited bioavailability and rapid metabolism, advanced delivery systems and artificial intelligence-enabled optimization strategies provide promising avenues to enhance therapeutic precision and scalability. This narrative review integrates mechanistic and translational insights to position RSV as a systems-oriented life-course intervention with sustained and intergenerational relevance in CVD. Full article

(This article belongs to the Special Issue Antioxidants in Cardiovascular Medicine: Emerging Trends and Future Perspectives)

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