Search Results (6,209)

Composite engine fan blades are critical aircraft engine components, and their failure can compromise the safe and reliable operation of the entire aircraft. To enhance aircraft availability and safety within a condition-based maintenance framework, effective methods are needed to identify damage and monitor the blades’ condition throughout manufacturing and operation. This paper presents a unique experimental framework for real-time monitoring of composite engine blades utilizing state-of-the-art structural health monitoring (SHM) technologies, discussing the associated benefits and challenges. A case study is conducted on a representative Foreign Object Damage (FOD) panel, a substructure of a LEAP (Leading Edge Aviation Propulsion) engine fan blade, which is a curved, 3D-woven Carbon Fiber Reinforced Polymer (CFRP) panel with a secondary bonded steel leading edge. The loading scheme involves incrementally increasing, cyclic 4-point bending (loading–unloading) to induce controlled damage growth, simulating in-operation conditions and allowing evaluation of flexural properties before and after degradation. External damage, simulating foreign object impact common during flight, is introduced using a drop tower apparatus either before or during testing. The panel’s condition is monitored in-situ and in real time by two types of SHM sensors: screen-printed piezoelectric sensors for guided ultrasonic wave propagation studies and surface-bonded Fiber Bragg Grating (FBG) strain sensors. Experiments are conducted until panel collapse, and degradation is quantified by the reduction in initial stiffness, derived from the experimental load-displacement curves. This paper aims to demonstrate this unique experimental setup and the resulting SHM data, highlighting both the potential and challenges of this SHM framework for monitoring complex composite structures, while an attempt is made at correlating SHM data with structural degradation. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

Blockchain technologies offer transformative potential in terms of addressing the security, trust, and identity management issues that exist in large-scale Internet of Things (IoT) deployments. This narrative review provides a comprehensive survey of various studies, focusing on decentralized identity management, trust mechanisms, smart contracts, privacy preservation, and real-world IoT applications. According to the literature, blockchain-based solutions provide robust authentication through mechanisms such as Physical Unclonable Functions (PUFs), enhance transparency via smart contract-enabled reputation systems, and significantly mitigate vulnerabilities, including single points of failure and Sybil attacks. Smart contracts enable secure interactions by automating resource allocation, access control, and verification. Cryptographic tools, including zero-knowledge proofs (ZKPs), proxy re-encryption, and Merkle trees, further improve data privacy and device integrity. Despite these advantages, challenges persist in areas such as scalability, regulatory and compliance issues, privacy and security concerns, resource constraints, and interoperability. By reviewing the current state-of-the-art literature, this review emphasizes the importance of establishing standardized protocols, performance benchmarks, and robust regulatory frameworks to achieve scalable and secure blockchain-integrated IoT solutions, and provides emerging trends and future research directions for the integration of blockchain technology into the IoT ecosystem. Full article

Tactile Internet (TI) is an innovative paradigm for emerging generations of communication systems that support ultra-low latency and highly robust transmission of haptics, actuation, and immersive communication in real time. It is considered a critical facilitator for remote surgery, industrial automation, and extended reality (XR). Originally intended as a flagship application for the fifth-generation (5G) networks, their strict constraints, especially the one-millisecond end-to-end latency, ultra-high reliability, and seamless adaptation, present formidable challenges. These challenges are the bottleneck for evolution to sixth-generation (6G) networks; thus, new architects and technologies are urgently required. This survey systematically discusses the most important underlying technologies for TI and immersive communications. It especially highlights using software-defined networking (SDN) and edge intelligence (EI) as enabling technologies. SDN improves the programmability, adaptability, and dynamic control of network infrastructures. In contrast, EI exploits intelligence-based artificial intelligence (AI)-driven decision-making at the network edge for latency optimization, resource usage, and service offering. Moreover, this work describes other enabling technologies, including network function virtualization (NFV), digital twin, quantum computing, and blockchain. Furthermore, the work investigates the recent achievements and studies in which SDN and EI are combined in TI and presents their effect on latency reduction, optimum network utilization, and service stability. A comparison of several State-of-the-Art methods is performed to determine present limitations and gaps. Finally, the work provides open research problems and future trends, focusing on the importance of intelligent, autonomous, and scalable network topologies for defining the paradigm of TI and immersive communication systems. Full article

Background: Major advances in antiretroviral therapy (ART) have transformed HIV into a chronic condition, yet drug resistance, long-term toxicities, adherence challenges, and persistent viral reservoirs continue to drive innovation. Objectives: To map and synthesize recent developments in anti-HIV drugs and delivery platforms with a focus on (i) new molecules in clinical development and (ii) novel mechanisms of action, following a scoping review framework aligned with PRISMA-ScR. Sources: We interrogated PubMed, Embase.com, Web of Science, and Scopus (January 2020–September 2025) and screened abstracts from CROI, IAS/AIDS, IDWeek, and HIV Glasgow (2023–2025). Content: The evidence base underscores capsid inhibition (lenacapavir) for multidrug-resistant HIV and its expansion into prevention, long-acting intramuscular maintenance with cabotegravir/rilpivirine, maturation inhibitors (zabofiravir), and attachment inhibition with fostemsavir. Broadly neutralizing antibodies (bNAbs) can sustain ART-free suppression in selected individuals. Ultra-long-acting delivery systems are advancing toward translational evaluation. Summary: The pipeline is diversifying toward less frequent dosing, new targets, and combination strategies. Successful and ethical implementation will require resistance-informed selection, equitable access, and reimagined healthcare delivery models that accommodate long-acting technologies. Full article

For any point P in the Euclidean plane of a triangle $\Delta$, the six parallelians of P lie on a single conic, which shall be called the parallelian conic of P with respect to $\Delta$. We provide a synthetic and an analytic proof of this fact. Then, we studied the shape of this particular conic, depending on the choice of the pivot point P. This led to the finding that the only circular parallelian conic is the first Lemoine circle. Points on the Steiner inellipse produce parabolae, and those on a certain central line yield equilateral hyperbolae. The hexagon built by the parallelians has an inconic $\mathcal{I}$ and the tangents of $\mathcal{P}$ at the parallelians define some triangles and hexagons with several circum- and inconics. Certain pairings of conics, together with in- and circumscribed polygons, give rise to different kinds of porisms. Further, the inconics and circumconics of the triangles and hexagons span exponential pencils of conics in which any pair of subsequent conics defines a new conic as the polar image of the inconic with regard to the circumconic. This allows us to construct chains of nested porisms. The trilinear representations of the centers of the appearing conics, as well as the perspectors of some deduced triangles, depending on the indeterminate coordinates of P, define some algebraic transformations that establish algebraic relations between well- and lesser-known triangle centers. We completed our studies by compiling a list of possible porisms between any pair of conics. Further, we describe the possible loci of pivot points so that the mentioned conics allow for porisms of polygons with arbitrary numbers of vertices. Full article

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

Ovarian follicular fluid (FF) is a metabolically active and biomarker-rich medium that mirrors the oocyte microenvironment. Its analysis is increasingly recognized in infertility diagnostics and assisted reproductive technologies (ART) for assessing oocyte competence, understanding reproductive disorders, and guiding personalized treatment. However, FF’s high viscosity, complex composition, and biochemical variability challenge reproducibility in sample preparation and molecular profiling. Sonication-based extraction has emerged as an effective approach to address these issues. By exploiting acoustic cavitation, sonication improves protein solubilization, metabolite release, and lipid recovery, while reducing solvent use and processing time. This review synthesizes recent advances in sonication-assisted FF analysis across proteomics, metabolomics, and lipidomics, emphasizing parameter optimization, integration with advanced mass spectrometry workflows, and emerging applications in microfluidics, automation, and point-of-care devices. Clinical implications are discussed in the context of enhanced biomarker discovery pipelines, real-time oocyte selection, and ART outcome prediction. Key challenges, such as preventing biomolecule degradation, standardizing protocols, and achieving inter-laboratory reproducibility, are addressed alongside regulatory considerations. Future directions highlight the potential of combining sonication with multi-omics strategies and AI-driven analytics, paving the way for high-throughput, standardized, and clinically actionable FF analysis to advance precision reproductive medicine. Full article

Arthroscopic shoulder surgery has undergone significant evolution over the past decades, particularly in the materials used for suture anchors. The transition from metallic to bioabsorbable polymer anchors has revolutionized soft tissue-to-bone repair procedures, offering distinct advantages in terms of biocompatibility, imaging compatibility, and reduced complications. This comprehensive review examines the current state-of-the-art in anchor polymers used in arthroscopic shoulder surgery and their biocomposite formulations. Additionally, we explore the role of biostable polymers and emerging technologies in anchor design. The review synthesizes clinical outcomes, degradation kinetics, biocompatibility profiles, and mechanical properties of various anchor polymer systems. We also discuss the challenges associated with each material type, including osteolysis, cyst formation, premature degradation, and osseointegration. Recent advances in biocomposite anchors demonstrate promising solutions to address these limitations, offering controlled degradation rates and enhanced osteoconductivity. This review provides clinicians and researchers with a comprehensive understanding of anchor polymer technologies, their clinical applications, and future directions in arthroscopic shoulder surgery. Nevertheless, potential publication bias and heterogeneity among studies should be considered when interpreting comparative data. Full article

Objective: This study aimed to assess whether oocyte donation in triplet pregnancies is associated with increased risk of placental abnormalities and pregnancy complications compared to triplet pregnancies conceived through assisted reproductive technology (ART) without oocyte donation. Methods: This single-center, retrospective, case–control study analyzed triplet pregnancies conceived via ART. The case group included pregnancies resulting from oocyte donation, while the control group comprised triplet pregnancies conceived by ART without oocyte donation. Maternal, obstetric, fetal, and neonatal outcomes were assessed. Gross and histopathological placental findings were evaluated using standardized criteria. Univariate and multivariate statistical analyses were performed. Results: A total of 77 triplet pregnancies (231 fetuses) were included: 29 in the oocyte donation group (87 fetuses) and 48 in the non-oocyte donation group (144 fetuses). Multivariate analysis revealed significantly higher rates of pregnancy-induced hypertension (p = 0.03), preeclampsia (p = 0.03), fetal growth restriction (p = 0.04), and fetal death (p = 0.01) in the oocyte donation group. Placental evaluation showed a higher frequency of infarcts (p = 0.04) and chronic inflammatory lesions—chronic villitis (p = 0.02) and chronic deciduitis (p = 0.03)—as well as signs of fetal vascular malperfusion, including avascular villi (p = 0.02) and stromal–vascular karyorrhexis (p = 0.01). Intervillous fibrin deposition was also more common in this group (p = 0.02). Conclusions: Oocyte donation in triplet pregnancies is associated with increased rates of placental abnormalities and adverse maternal and fetal outcomes when compared with ART without oocyte donation. Placental examination may provide valuable insights into the mechanisms involved. Further research is warranted to clarify the underlying immunological and vascular pathways. Synopsis: In our cohort of 77 triplet pregnancies, those conceived via oocyte donation showed significantly higher rates of preeclampsia, fetal growth restriction, and fetal death. Placental examination revealed more chronic villitis, deciduitis, intervillous fibrin, avascular villi, and stromal–vascular karyorrhexis, suggesting immune and vascular dysfunction in oocyte donation pregnancies. Full article

In the realm of Industry 5.0, the incorporation of Artificial Intelligence (AI) in overseeing workers, machinery, and industrial systems is essential for fostering a human-centric, sustainable, and resilient industry. Despite technological advancements, the construction industry remains largely labor intensive, with site management and interventions predominantly reliant on manual judgments, leading to inefficiencies and various challenges. This research emphasizes identifying unsafe behaviors and risks within construction environments by employing AI. Given the continuous emergence of unsafe behaviors that requires certain caution, it is imperative to adapt to these novel categories while retaining the knowledge of existing ones. Although deep convolutional neural networks have shown excellent performance in behavior recognition, they traditionally function as predefined multi-way classifiers, which exhibit limited flexibility in accommodating emerging unsafe behavior classes. Addressing this issue, this study proposes a versatile and efficient recognition model capable of expanding the range of unsafe behaviors while maintaining the recognition of both new and existing categories. Adhering to the continual learning paradigm, this method integrates two types of complementary prompts into the pre-trained model: task-invariant prompts that encode knowledge shared across tasks, and task-specific prompts that adapt the model to individual tasks. These prompts are injected into specific layers of the frozen backbone to guide learning without requiring a rehearsal buffer, enabling effective recognition of both new and previously learned unsafe behaviors. Additionally, this paper introduces a benchmark dataset, Split-UBR, specifically constructed for continual unsafe behavior recognition on construction sites. To rigorously evaluate the proposed model, we conducted comparative experiments using average accuracy and forgetting as metrics, and benchmarked against state-of-the-art continual learning baselines. Results on the Split-UBR dataset demonstrate that our method achieves superior performance in terms of both accuracy and reduced forgetting across all tasks, highlighting its effectiveness in dynamic industrial environments. Full article

Assisted reproduction is a rapidly expanding pillar of medical tourism. Greece combines a liberal legal framework, internationally accredited clinics, and comparatively competitive costs, attracting cross-border patients seeking ART services. Following the 2022 amendment (Law 4958/2022) which amends the original law n.3305/2005, treatment is permitted up to age 54 under specific authorization, while court-approved surrogacy, anonymous gamete donation, and the adoption of decision-support technologies (e.g., AI-assisted embryo assessment, PGT-A) underpin the sector’s growth. This review synthesizes legal, medical, economic, and social dimensions, drawing on Q1 literature and official datasets (WHO, OECD, ESHRE/ICMART), and compares Greece with Spain, the USA, the Czech Republic, and Ukraine. Quantitative indicators include age-stratified success rates and indicative treatment costs. We discuss benefits and risks for patients and the health system, highlighting policy options for sustainable, ethically robust reproductive tourism in Greece. Full article

Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged as a promising Layer 1 approach by partitioning blockchain networks into smaller, parallelized components, significantly enhancing processing efficiency while maintaining decentralization and security. In this paper, we have conducted a systematic literature review, resulting in a comprehensive review of sharding. We provide a detailed comparative analysis of various sharding approaches and emerging AI-assisted sharding approaches, assessing their effectiveness in improving TPS and reducing latency. Notably, our review is the first to incorporate and examine the standardization efforts of the ITU-T and ETSI, with a particular focus on activities related to blockchain sharding. Integrating these standardization activities allows us to bridge the gap between academic research and practical standardization in blockchain sharding, thereby enhancing the relevance and applicability of our review. Additionally, we highlight the existing research gaps, discuss critical challenges such as security risks and inter-shard communication inefficiencies, and provide insightful future research directions. Our work serves as a foundational reference for researchers and practitioners aiming to optimize blockchain scalability through sharding, contributing to the development of more efficient, secure, and high-performance decentralized networks. Our comparative synthesis further highlights that while Bitcoin and Ethereum remain limited to 7–15 TPS with long confirmation delays, sharding-based systems such as Elastico and OmniLedger have reported significant throughput improvements, demonstrating sharding’s clear advantage over traditional Layer 1 enhancements. In contrast to other state-of-the-art scalability techniques such as block size modification, consensus optimization, and DAG-based architectures, sharding consistently achieves higher transaction throughput and lower latency, indicating its position as one of the most effective Layer 1 solutions for improving blockchain scalability. Full article

This study constructs a comprehensive analytical framework for fire evacuation efficiency in high-rise buildings based on risk management theory, environment–behavior relationship theory, and stress-cognition theory. Through a systematic literature review and three rounds of Delphi expert consultation, a measurement questionnaire for fire-escape behavior was developed, ultimately screening out 35 key measurement items. Data were collected from 248 residents of high-rise residential buildings in Beijing who had experienced fires. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) were employed to validate the model. The results show that the fire emergency management system (FEMS) and building-safety performance planning (BSPP) have a significant positive impact on escape response behavior (ERB), while situational panic psychological perception (SPPP) has a negative impact. The study also finds that emergency-response training and diversified escape-route design are key driving factors, and cognitive bias significantly affects situational panic psychological perception. This research provides empirical support for fire-escape management in high-rise buildings and develops a reliable measurement tool. Full article

This study aimed to assess, compare, and validate a satellite-based plant health and water stress indicator: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) over Uganda. We used a direct agricultural drought indicator—the Standardized Precipitation and Evapotranspiration Index at scale 3 (SPEI-03)—and a plant water stress indicator—the crop water stress index (CWSI)—for the period of 1983–2013. Novel approaches such as spatial variability and trend analysis, along with correlation analysis, were used to achieve this. The results showed that there are six classes of highly variable photosynthetic activity over Uganda, dominated by class 4 (0.36–0.45). This dominant class encompassed 45% of the total land area, mainly spanning cropland. In addition, significant increases in monthly photosynthetic activity (FAPAR) and FAPAR-centered stress indicators (SFI < −1) were observed over 85% and 60% of total land area, respectively. The Standardized FAPAR Index (SFI) had a strong positive correlation with SPEI-03 over cropland, grassland, and forest lands, while SFI had a strong negative correlation with CWSI over 80% of the total area. These results highlight the state and variation in plant health and water stress, generate insights on ecosystem dynamics and functionality, and weigh in on the usability and reliability of satellite-based variables such as FAPAR in plant water monitoring over Uganda. We thus recommend satellite-based FAPAR as a robust proxy for vegetation health and water stress monitoring over Uganda, with potential application in crop yield prediction and irrigation management to inform effective agricultural planning and improve productivity. Full article