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The integration of Artificial Intelligence (AI) in turbomachinery and fan systems is transforming traditional design, diagnostics, and operational strategies. Artificial Intelligence allows for the efficient exploration of wide design space, easy and fast prediction of fan performance and improving existing system operation and maintenance. Nevertheless, this AI-driven revolution still raises concerns and diffidence in the community, as highlighted by the results of a survey delivered to over 100 fan experts and discussed in this paper. This manuscript aims to provide an overview of Fan-AI applications through a comprehensive literature review of notable use cases. The applications target different stages of the life cycle of fans, from ML-assisted three-dimensional design/optimization to data-driven performance prediction, AI-driven fan control and fault analysis/prognosis. For each of these categories, the relevant application are discussed, highlighting trends, adopted algorithms and strategies, as well as limiting factors. This study also shares the views of experts on both fan design, optimization and operations and AI methods in the upcoming challenges for fan industry. Starting from the need of high-quality data, the improvement of model generalization and the embedding of Fan-AI in the standard engineering practices. This paper concludes with a discussion on the future role of AI in fans, suggesting pathways for research and industrial adoption that balance technological innovation with domain-specific constraints. Full article

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are promising enablers of ultra-reliable and low-latency communication in next-generation wireless networks. However, the initial access and beam alignment process remains challenging because highly directional beams must be rapidly aligned in a three-dimensional environment. In this paper, we investigate a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems, where user equipment scans a 3D spherical region to detect UAV base stations. The objective is to jointly minimize the expected cell-search latency and its variance while satisfying detection-failure and link-quality constraints. To solve this non-convex optimization problem efficiently, we employ the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), which combines Lévy-flight exploration with self-renewal to improve convergence robustness. A unified propagation model is adopted to cover both mmWave and THz regimes by incorporating free-space spreading loss and frequency-dependent molecular absorption. Extensive Monte Carlo simulations compare the proposed approach with Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods. The results show that LSRFDA achieves lower latency, lower latency variation, more reliable detection, and lower energy consumption across a wide range of UAV densities and coverage radii. These outcomes highlight the effectiveness of risk-aware geometric optimization for fast and dependable initial access in UAV-assisted 5G mmWave and 6G THz networks. Full article

Background: Digital health technologies are increasingly integral to healthcare delivery worldwide; however, successful adoption depends on more than technological availability. In nursing, readiness is particularly important because digital systems increasingly shape documentation, communication, decision support, and care delivery. Within the context of Saudi Arabia’s healthcare transformation, doctoral nursing students are positioned as future educators, clinicians, and leaders whose perceptions can provide insight into digital health readiness and preparation. Aim: This study aimed to explore doctoral nursing students’ perceptions of their readiness to adopt digital health technologies in Saudi Arabia, guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: This exploratory, qualitative, descriptive study recruited 9 doctoral nursing students from a public university in Saudi Arabia using purposive sampling based on predefined eligibility criteria. Individual semi-structured interviews were conducted online and audio-recorded. Data were analyzed using a hybrid inductive–deductive thematic approach. UTAUT2 informed the deductive component of the analysis, while inductive coding and cross-case comparison supported theme generation. Results: Four interrelated themes were identified. First, readiness was positive but conditional, shaped by movement from openness to professional necessity, familiarity, workflow fit, and caution about the possible weakening of foundational or manual competence. Second, adoption depended on practical value and system credibility, including access, convenience, efficiency, safety, documentation integrity, accuracy, privacy, and reliability. Third, adoption was organizationally mediated through leadership, peer culture, infrastructure, implementation conditions, training, follow-up, and academic preparation. Fourth, digital health was understood as supporting, not substituting for, nursing work by reducing avoidable burden and creating more space for direct care while preserving human presence, communication, and clinical judgment. Conclusions: In this sample of doctoral nursing students, digital health readiness was positive but conditional. The findings suggest that readiness reflects a context-sensitive professional judgment shaped by educational preparation, organizational support, system credibility, workflow compatibility, and the perceived ability of digital technologies to enhance nursing work rather than replace it. Implications: The findings suggest that nursing education and practice should strengthen applied digital health competencies through simulation-based preparation, electronic documentation training, privacy and ethics education, workflow-aligned implementation, and sustained organizational support. Full article

Bubble entropy has established its own place in the research community, representing a new and promising definition of entropy. Based on the work required to order a vector in an embedding space of dimension m, Bubble entropy gives a physical interpretation of what the metric actually computes. In this work, Bubble entropy is evaluated based on its ability to classify RR time series, the time series most commonly considered for entropy-based analysis in the field of biomedical engineering. For this purpose, it is compared with three other definitions of entropy: the most widely used Sample entropy and Approximate entropy, the most relative to Bubble entropy, and also the widely used Permutation entropy. Signals from healthy individuals, in sinus rhythm, are compared with signals from cardiac patients, and machine learning methods are applied to calculate the classification accuracy that each method can achieve. The classifiers chosen are k-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gaussian Naive Bayes. Feature evaluation methods are also employed to serve as additional measures of effectiveness. Bubble entropy generally manages to achieve better results than Sample entropy, Approximate entropy and Permutation entropy, both in terms of classification accuracy and feature ranking. Full article

Radio-over-Fiber (RoF) integrated with Free-Space Optical (FSO) communication as a fronthaul is a promising solution for next-generation wireless systems, but severely suffers from the frequency-selective characteristics of hybrid RoF-FSO channels. This paper presents a measurement-driven, deployment-oriented optimization that jointly performs structure-aware pilot placement and sixth-order polynomial regression channel interpolation to enhance spectral efficiency and signal quality in quasi-static indoor FSO environments. Differential channel analysis across three transmission scenarios—Electrical Back-to-Back (B2B), Fiber B2B, and FSO—identifies critical subcarriers with high frequency-selective variation that require dense pilot allocation. A gradient-based algorithm positions 50 pilots with dense spacing (every 3 subcarriers) in critical regions and sparse spacing (every 9 subcarriers) in stable regions, reducing pilot overhead by 26.5% and increasing data capacity by 5.3% (340 → 358 subcarriers) compared to uniform placement of 68 pilots. Sixth-order polynomial regression models the non-linear channel frequency response, overcoming limitations of conventional linear interpolation. Experimental validation on a 4-QAM RoF-OFDM system over 40.6 MHz bandwidth shows that structure-aware pilot placement alone reduces Error Vector Magnitude (EVM) by 15.9%, while polynomial regression alone improves it by 15.7%. Combined optimization of structure-aware pilot placement with polynomial regression interpolation achieves 23.5% EVM reduction and 460× lower BER, equivalent to 3.2 dB SNR gain at BER = ${10}^{-6}$. Comparative analysis of four system configurations confirms consistent performance advantages across SNRs of 12–30 dB. The proposed measure-once, optimize-forever paradigm requires only one-time channel characterization, making it suitable for short-range controlled quasi-static indoor FSO links in 5G/6G fronthaul, optical wireless networks, and inter-building backhaul applications. Full article

Total Electron Content (TEC) is a key parameter for characterizing the state of the ionosphere, and its spatiotemporal variations can significantly affect satellite navigation, radio communication, and space weather monitoring. To address the pronounced diurnal periodicity in global TEC map forecasting and the commonly neglected continuity at longitudinal boundaries, this study proposes an encoder–decoder ConvLSTM model that integrates periodic-matched residual prediction with longitude-circular boundary-aware convolution, namely the Longitude-Circular Periodic-Residual ED-ConvLSTM (LC-PR-EDConvLSTM). In the proposed model, the TEC map at the same temporal phase on the previous day is used as a periodic background field, enabling the network to focus on learning the residual variation in future TEC relative to this background. Meanwhile, longitude-circular padding is introduced into the convolution operations to preserve the spatial continuity of global TEC maps across the −180° and 180° meridians. Experiments were conducted using CODE global ionospheric map products from 2009 to 2019, with 12 TEC maps from the previous day used as inputs to predict 12 TEC maps for the following day. The results show that LC-PR-EDConvLSTM achieves RMSE values of 3.68 TECU and 1.37 TECU on the 2015 high-solar-activity test set and the 2019 low-solar-activity test set, respectively, outperforming the C1pg, ED-ConvGRU, and ED-ConvLSTM benchmark models. Ablation experiments further verify the effectiveness of the periodic-matched residual prediction strategy and the longitude-circular boundary-aware convolution. Analyses of typical space weather events and latitudinal regions demonstrate that the proposed model provides stable forecasting performance under complex space weather conditions and across most latitude regions. Full article

As smart city approaches increasingly extend to public open spaces, understanding what urban park users expect from digital park applications has become a critical issue for sustainable urban management. This study examines park users’ expectations of smart park applications through an exploratory sequential mixed-methods design. In the first phase (Study I), semi-structured interviews were conducted with 32 purposively selected participants representing four user groups—parents with children, sport-oriented users, older adults, and general adults—in urban parks in Eskişehir, Türkiye. Thematic analysis identified eight user expectation themes, which were subsequently operationalized into a seven-factor quantitative structure. In the second phase (Study II), a seven-factor scale derived from the qualitative findings was administered to 374 participants. Confirmatory factor analysis demonstrated a good overall model fit, and the scale exhibited strong reliability and convergent validity. One-way ANOVA revealed significant between-group differences in six of the seven dimensions, with sport-oriented users consistently reporting higher expectations than older adults. Safety and Activity Diversity was the only dimension showing no significant group differences, indicating a universal expectation across all user profiles. Multiple regression analysis showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function and Safety. Integration of both phases through a joint display revealed that expectations are both universal and user profile-specific, underscoring the need for user-sensitive smart park design. By linking digital park services to user expectations, well-being-oriented park design, and the sustainable use of urban green spaces, these findings contribute to the literatures on smart cities, urban green spaces, and well-being, providing an empirically informed and user-centred framework for digital park applications that may inform efforts toward healthier, more inclusive, and more sustainable urban public spaces in line with SDGs 3 and 11. Full article

With the advancement of wireless communication technologies, especially the emergence of mobile communication technologies such as satellite internet and sensor networks, the rapid proliferation of communication facilities has given rise to challenges such as the scarcity of spectrum bandwidth resources, heightened channel interference, and increased noise. Consequently, traditional image source coding technologies urgently require further improvements in their compression ratio and anti-interference capability. Targeting image transmission scenarios characterized by low signal-to-noise ratios and constrained channel bandwidths, this paper proposes an image semantic coding method based on the pre-trained Stable Diffusion model, producing a zero-shot universal image compressor. This compressor leverages the denoising network of the Stable Diffusion model, with feedback from channel SNR, to further enhance the adaptability of transmitted data to channel interference. Additionally, by designing quantization and entropy coding methods for feature tensors in the semantic space, the compression ratio of the image coding process is further improved. Simulation results demonstrate that the proposed method not only achieves superior compression performance but also ensures relatively high similarity between the decoded reconstructed image and the original. Notably, it delivers a significant improvement in the perceptual similarity of human visual quality. Furthermore, the method can adapt to Gaussian noise channels, Rician fading channels, and Rayleigh fading channels with low SNR, exhibiting broad application prospects in the field of wireless communication coding methods, where the electromagnetic environment is growing increasingly complex. Full article

The development of multifunctional and lightweight materials is increasingly shaping the design of next-generation sensing and communication systems for space applications. In CubeSat platforms, severe constraints on mass, volume, and structural complexity motivate the integration of antenna functionalities directly onto load-bearing structures. In this context, printed electronics, particularly inkjet-printed conductive materials, offer new opportunities for creating adaptive, flexible, and structure-integrated devices that support both sensing and communication functionalities. This work investigates the electrical performance of inkjet-printed conductive materials for structure-integrated patch antennas. Two silver-based inks and one carbon-based ink were deposited on fiberglass-reinforced epoxy substrates and electrically characterized over a temperature range from −20 °C to 50 °C, representative of CubeSat operational conditions. The silver-based inks exhibited electrical conductivities in the range of 10⁶ S/m with limited variation (<10%) under thermal cycling, whereas the carbon-based ink remained below 10¹ S/m, even after multilayer deposition, indicating insufficient performance for this application. Based on these results, the best-performing silver ink was selected to fabricate a proof-of-concept patch antenna directly on an S2-glass/epoxy structural substrate. The proposed approach demonstrates the feasibility of integrating conductive inkjet-printed layers onto composite structural substrates intended for future structure-integrated antenna applications in CubeSat platforms, offering a pathway toward mass-efficient, low-profile, and highly integrated communication structures. Full article

Research on sensitive human narratives is increasingly constrained by ethical and privacy regulations that limit access to primary data, creating a structural small-data challenge that limits deep computational analysis. To address this limitation, this study validates a Natural Language Processing protocol that scales 946 real breakup narratives from r/breakups to 6000 human-validated high-fidelity synthetic records across five BERTopic clusters. The architecture employs MPNet, UMAP, and HDBSCAN to map latent space and thematically cluster texts, extracts seed documents using the Kneedle algorithm, and orchestrates DeepSeek V3.2 with stochastic sampling and small batches (k = 5). Automated validation via Cosine Similarity with a P10 threshold attained a mean semantic similarity of 0.7204 (range 0.6413–0.7855) and a fidelity rate of 99.08%. Expert human review by two researchers of this investigation evaluated 1732 posts on topic adherence and emotional authenticity using Gwet’s AC2. Five of six clusters achieved AC2 ≥ 0.70 on both dimensions; Topic 3 showed marginal adherence (AC2 = 0.660) while maintaining acceptable authenticity (AC2 = 0.817), and the 1200 synthetic posts for Topic 5 failed human validation (AC2 < 0.50) due to documented LLM safety-filter limitations and are excluded from the final corpus. These results demonstrate that the proposed protocol enables the research community to generate validated, privacy-preserving synthetic data ecosystems while establishing empirical boundary conditions for sensitive topic analysis. Full article

The shift toward hybrid intelligence in learning systems emphasizes the integration of human and AI cognitive capabilities into unified problem-solving processes. Yet, design principles for enabling such systems in immersive learning environments remain insufficiently understood. Immersive learning environments, realized through extended reality (XR), introduce unique affordances and challenges for embodied interaction, spatial communication, and co-presence that demand rethinking how collaboration unfolds. This conceptual paper proposes a framework and a set of design commitments for enabling sensible Human–AI collaboration for hybrid intelligence in immersive learning environments. Drawing on research and theories in Human–AI teaming and Human–AI collaboration, XR interaction design, learning sciences, and cognitive ergonomics, we identified four key dimensions of collaboration: collaborative agency and role distribution, shared attention and regulation, embodied and spatial interaction, and mutual intelligibility and adaptive support. We outline a conceptual framework describing how humans and AI can jointly achieve goals, negotiate roles, coordinate attention, and engage in knowledge co-construction within immersive learning spaces for hybrid intelligence. We further argue that immersive contexts require new forms of mutual intelligibility, spatial communication, and adaptive support to enable hybrid intelligence characterized by adaptive co-intelligence that improves learning processes in real time. Further, we advance a definition of hybrid intelligence specific to immersive learning that identifies three emergent properties: collaborative fluency, adaptive co-presence, and distributed knowledge growth. The paper closes with implications for researchers and practitioners and identifies constitutive design tensions that future work would navigate. Finally, this paper is conceptual in nature; the framework presented is offered as a theoretically grounded hypothesis for future empirical inquiry. Future research directions include validating the emergent properties through observational and experimental studies in actual XR environments and developing measurement tools adequate to the spatial, embodied, and real-time dimensions the framework identified. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

This paper develops a finite-state mathematical framework for structural stability and regime classification in discrete-time state–event–response systems whose effective transition structure is generated endogenously by state-dependent response rules. Unlike classical structural stability theory, which focuses on qualitative persistence in smooth dynamical systems, and unlike Markov-chain analysis, which typically assumes a fixed transition kernel, the proposed framework treats the transition graph as an induced object. The model specifies a finite state space, an event-generation law, an elasticity-dependent attenuation function, and a deterministic transition mapping. Structural regimes are classified by adjacency relations, communicating components, absorbing organization, and long-run occupancy support. A Monte Carlo verification layer is used only to examine whether the analytically defined topological regimes are visible in finite-sample occupancy signatures. The results indicate that, within the finite-state setting considered here, admissible disturbance scaling changes traversal frequency without changing graph identity, whereas elasticity variation can activate or deactivate effective edges and thereby generate structurally distinct regimes. Full article

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality (MR) technology offers a compelling approach for designing innovative models of human–robot interaction (HRI). For robot manipulators, mobile MR frameworks that allow a user to communicate a goal position for the robot’s end effector have been widely studied. However, HRI applications that may require other relevant information for the manipulator to complete more complex tasks remain unexplored. Thus, we propose an MR-enhanced HRI framework, deployed on a touchscreen tablet, that utilizes a virtual arrow object to communicate force intent (i.e., location, direction, and magnitude) to the manipulator and provide visual force feedback to the user. To evaluate the system performance and user experience, we conducted a user study with 25 participants who used a manipulator robot to complete four insertion subtasks, reporting a task success score of 96%, a usability overall mean score of 4.35 out of 5, and a low task load index of 21.49 out of 100. The results show that the MR-HRI framework is intuitive to operate, allowing users to successfully perform assigned tasks by effectively communicating their intentions through the virtual arrow. Full article

This article examines the spiritual, intellectual, and institutional legacy of Caterina Vigri (1413–1463) and the formation of a “school of holiness” within the Poor Clare monastery of Corpus Domini in Bologna. Through the analysis of key texts produced within the monastic milieu—including the Libro devoto (later known as The Seven Spiritual Weapons), the Ordinazioni, the epistolary Formulario, and the Book of Visions and Revelations by Valeria Campanazzi—the study explores how Vigri’s teachings were transmitted, received, and reworked across generations of nuns. Particular attention is devoted to the centrality of obedience as the defining principle of monastic life, which marks a significant shift from earlier Franciscan emphases on poverty. The article highlights the pedagogical dimension of these writings, their grounding in Sacred Scripture, and their role in shaping a collective religious identity within an Observant context. At the same time, it situates Vigri’s spiritual program within broader developments in late medieval and early modern Christianity, including the institutional consolidation of religious life and the circulation of diverse spiritual influences. By tracing both continuity and transformation within the Corpus Domini community, the study demonstrates the existence of a sustained intellectual and devotional tradition that extended well beyond the founder’s lifetime. The “school of Caterina” thus emerges as a dynamic space of female religious authority, literary production, and theological formation. Full article