Search Results (192)

Background: Endometriosis affects 10–15% of reproductive-aged women and is a leading cause of infertility through anatomical, inflammatory, and molecular mechanisms. Objective: This review synthesizes current evidence on the pathophysiology of endometriosis-associated infertility and evaluates medical, surgical, and ART strategies to guide individualized management. Methods: We conducted a narrative review (2000–2025; PubMed, Scopus, WoS) synthesizing RCTs, meta-analyses, and observational studies on mechanisms and treatment outcomes. Results: In rASRM stage III–IV, tubo-ovarian distortion and adhesions mechanically impair oocyte pickup and embryo transport. In superficial disease, animal models demonstrate that peritoneal inflammatory mediators and ROS can impair oocyte maturation, though direct causal evidence in humans is lacking. Epigenetic dysregulation has been identified in the eutopic endometrium and linked to progesterone resistance, though its direct causal role in infertility remains unestablished. Hormonal suppression controls pain but does not improve spontaneous conception rates. Laparoscopic surgery in stage I–II remains debated. Prospective evidence supporting fertility benefit from DIE excision without mechanical obstruction is lacking. Cystectomy consistently reduces AMH, favoring IVF over surgery unless symptoms or retrieval barriers exist. IVF/ICSI live birth rates per cycle in stage I–II are comparable to those without endometriosis; cumulative rates after ≤5 cycles reach 43–46% in treated vs. 28% in untreated stage III–IV patients. Conclusions: Management requires two sequential decisions: first, whether to perform a diagnostic laparoscopy to identify minimal disease and adhesions, and second, whether to proceed with surgery or transfer directly to ART. Age ≥ 35, infertility > 2 years, or low AMH/AFC favor immediate IVF. Post-surgical EFI guides timing: high EFI supports expectant management or IUI; low EFI should prompt ART referral. When cystectomy is necessary, tissue-sparing techniques should be prioritized and fertility preservation, including oocyte cryopreservation, discussed preoperatively. Full article

The rapid advancement of Generative Artificial Intelligence (Gen-AI) is fundamentally reshaping labor markets, creating an urgent need for higher education institutions to adapt their program capacities and curricula. This paper proposes a data-driven Robust Multi-Objective Planning (RMOP) framework to translate heterogeneous Gen-AI labor shocks into actionable, program-level decisions regarding enrollment scaling and curriculum design. Grounded in O*NET micro-task structures, we model occupational evolution as a dynamic system of substitution, augmentation, and insulation driven by logistic technology diffusion. Our simulations across STEM, trade, and arts occupations reveal sharply divergent trajectories: Information Security Engineers face a 62% total impact dominated by substitution, whereas Electricians retain over 80% insulation, and Musicians experience high exposure but low substitution. To bridge these macro-level forecasts with immediate institutional maneuvers, the framework couples an AI-adjusted Grey Model (GM(1,1)) demand model with a Program Effectiveness Index (PEI) to yield discrete enrollment policy levers (Expand, Contract, and Adjust). For curriculum optimization, we employ Ridge regression to rank employability-related curriculum drivers and NSGA-II to generate Pareto portfolios under competing institutional objectives, including employability, instructional cost, ethics, and environmental impact. Final implementable recommendations are selected through entropy-weighted TOPSIS, where student well-being and education equity are treated as supplementary decision criteria rather than direct prediction targets. In addition, an Automation Risk Score (ARS) and a K-means TC clustering module are used to illustrate potential transfer paths across broader institutional settings. Internal scenario checks show that the AI-adjusted GM(1,1) reduces average hold-out MAPE from 7.0% to 5.8% relative to the baseline GM(1,1), and that NSGA-II achieves slightly stronger Pareto coverage than MOPSO and MODE under the same curriculum-portfolio setting. These checks are interpreted as preliminary decision-support evidence rather than external predictive validation. Overall, RMOP is presented as a scenario-based decision-support framework that links Gen-AI occupational exposure, enrollment adjustment, and curriculum portfolio design. Full article

Floods are among the most catastrophic natural disasters globally, causing significant damage to both life and infrastructure. Consequently, immediate and accurate assessment of inundated areas is critical for effective emergency response. While optical remote sensing is typically used for flood assessment, it is often ineffective during active flood events due to persistent cloud cover and precipitation. To address this, this research develops a deep learning method utilizing Synthetic Aperture Radar (SAR), which offers all-weather, 24 h imaging capabilities. Specifically, an attention-based differential Siamese U-Net was developed to detect temporal changes in bi-temporal SAR imagery (e.g., Sentinel-1) acquired before and after flood events. The method was evaluated on the S1GFloods dataset, comprising 5360 bi-temporal Sentinel-1 SAR image pairs across 46 flood incidents on six continents. Experimental results demonstrate a flood Intersection over Union (IoU) of 92.43%, an F1 score of 96.07%, and a recall of 97.64%. These metrics rank the proposed approach third overall among top-performing methods on this dataset. Notably, the high recall rate indicates the model is particularly beneficial for emergency response, as it minimizes the number of undetected flooded areas. Despite utilizing a CNN-based architecture that is less complex than Vision Transformer models, this method achieves results comparable to the state-of-the-art DAM-Net, with a performance difference of only 0.77%. Full article

This article reviews the state of the art of laser ablation and deposition techniques applied so far to more than 50 elements, including metals, metalloids and lanthanides, yielding a wide variety of compounds in the form of thin films. Laser deposition processes have been performed in high-vacuum (HV) reactors at pressure values ranging between 10⁻¹ and 10⁻⁵ Pa, namely pulsed laser deposition (PLD), or, under different reactive gas ambient (O₂, N₂, CH₄, NH₃ and many others), so-called reactive pulsed laser deposition (RPLD), with the aim to form thin films with desirable chemical compositions. While a few metals have not been deposited as pure metallic films because they have no immediate technological interest, others, like alkali and alkaline earth metals, cannot be deposited in pure metallic form due to their very strong reactivity with oxygen, water vapor and hydrogen molecules which are always present, even in ultra-high-vacuum (UHV) systems, at pressure values of 10⁻⁵–10⁻¹⁰ Pa. Furthermore, elements of the Mendeleev periodic table with an atomic number higher than 88, such as actinides and synthetic elements, are dangerous to handle and deposit in the form of thin films due to their high radioactivity; therefore, they are excluded from this review. The inclusion of the non-metal thin films of carbon (C) and related chemical compounds prepared by PLD and RPLD in the present review is justified by the extensive research and the numerous scientific articles reported in the field. All the results obtained by PLD and RPLD techniques so far are discussed and presented in tabular format to guide the reader. Full article

Preserving ancient languages is essential for understanding the cultural and linguistic heritage of humanity. Old English, however, remains critically under-resourced, which limits its accessibility to modern natural language processing (NLP) techniques. We present a scalable framework that uses advanced large language models (LLMs) to generate high-quality Old English texts to address this gap. In this study, we specifically employ state-of-the-art models, including Llama-3.1-8B and Mistral-7B, as our foundation models, which are then adapted to the unique characteristics of Old English. Our approach combines parameter-efficient fine-tuning (Low-Rank Adaptation (LoRA)), data augmentation via back-translation, and a dual-agent pipeline that separates content generation (in English) and translation (into Old English). Evaluation with automated metrics (BLEU, METEOR, and CHRF) shows improvements over baseline models, with BLEU scores increasing from 26 to over 65 for English-to-Old English translation. Expert human assessment confirms high grammatical accuracy and stylistic fidelity in the generated texts, with average scores of 9.0/10 for inflection and word order, 9.1/10 for lexical authenticity, and 7.8 for semantic coherence. These results demonstrate that the framework can reliably expand limited historical corpora while maintaining linguistic integrity, with immediate practical applications in digital humanities research, computational philology, and the development of educational resources for Old English study. Beyond expanding the Old English corpus, our method offers a practical blueprint for revitalizing other endangered languages, thus linking AI innovation with the goals of cultural preservation. Full article

While Graph Autoencoders (GAEs) have become a standard for unsupervised representation learning, their reliance on integer-order convolutions inherently restricts information propagation to immediate local neighborhoods. This paper introduces the Fractional Graph Autoencoder (FGAE) and its variational extension (FVGAE) to move beyond these local constraints. By integrating fractional Laplace operators, our framework generalizes conventional GAEs and enables tunable non-local propagation. We show that the fractional order $\alpha$ acts as a structural regularizer, utilizing the Green’s function of anomalous diffusion to induce a form of structural memory within the latent space. This allows the model to recover long-range dependencies that are typically lost in standard architectures. Systematic benchmarking across eight datasets—ranging from homophilic citation networks to heterophilic and dense product graphs—shows that these fractional variants consistently outperform both foundational and state-of-the-art baselines (ARGA, SIG-VAE, and GraphMAE). Notably, on the Amazon Computers and Citeseer datasets, our methods achieve relative increases in Normalized Mutual Information (NMI) of 77.55% and 67.28%, respectively. Statistical analysis confirms these gains are robust, with large effect sizes (Cohen’s $d>0.80$) and significance at $p<0.05$. These findings suggest that fractional graph autoencoding offers a mathematically grounded inductive bias for capturing the complex, multi-scale dynamics of real-world networked systems. Full article

Competence-oriented Education for Sustainable Development requires evidence that immersive and gamified learning experiences elicit sustainability-relevant change beyond short pre–post windows. This study examines the Art Nouveau Path, a location-based mobile augmented reality heritage game implemented in Aveiro, Portugal, using a four-wave repeated cross-sectional design with anonymous student samples: baseline (S1-PRE, N = 221), immediate post-activity (S2-POST, N = 439, validated n = 438), follow-up (S3-FU, N = 434), and distant follow-up (S4-DFU, N = 69, validated n = 67). Analyses were anchored in a shared 25-item GreenComp-based questionnaire (GCQuest) block targeting Embodying Sustainability Values (ESVs; scale of 1 to 6) and combined distribution-aware descriptives, nonparametric omnibus, and pairwise tests with Holm correction, and planned robustness checks including equal-n downsampling and alternative scoring. Results displayed a pronounced post-activity peak (S2-POST), partial attenuation at follow-up (S3-FU), and convergence toward baseline at distant follow-up (S4-DFU), accompanied by loss of the high-agreement tail. Item-level contrasts suggested that later-wave declines concentrated in effortful self-regulation and critical appraisal items, whereas value endorsement items were more stable. These findings indicate that field-deployable mobile AR heritage paths may generate strong proximal competence-aligned signals; nevertheless, durable enactment-oriented change is likely to require structured reinforcement and integration into broader curricular sequences. Full article

While Digital Twins offer transformative potential, their efficacy for real-time control is constrained by the slow data acquisition and the high computational intensity required to process raw datasets like point clouds. This paper identifies these critical bottlenecks—specifically the latency between data capture and actionable insight—and proposes a refined theoretical framework for near-real-time automated progress monitoring and quality reporting. Building on the findings of the NORMENG project and informing the subsequent AutoGreenTraC project, this research synthesizes state-of-the-art advancements in reality capture, including LIDAR, SfM-MVS, and 360-degree vision. The study highlights a fundamental divergence in stakeholder requirements: the need for millimeter-level precision in quality control versus the demand for high-velocity documentation for progress monitoring. A key innovation presented is the shift toward neural rendering techniques to bypass the computational delays of traditional photogrammetry and enable immediate on-site visualization. By structuring a tiered processing hierarchy that combines lightweight edge analysis for immediate safety and progress monitoring with asynchronous high-fidelity Digital Twin updates, the framework aims to establish a single source of truth. Full article

Ohmic contact resistance is a persistent and increasingly dominant bottleneck limiting the practical performance of wide-bandgap (WBG) and ultrawide-bandgap (UWBG) power semiconductor devices. This review provides a comprehensive and comparative treatment of specific contact resistivity (${\rho }_c$) phenomena across five material systems—4H-SiC, GaN, β-Ga₂O₃, AlN/AlGaN, and diamond—spanning fundamental contact physics, characterization methodology, material-specific state of the art, device context, and advanced engineering strategies. A semi-empirical scaling analysis establishes that the minimum achievable ${\rho }_c$ increases by approximately one order of magnitude per 0.8–1.0 eV increase in bandgap, arising from the interplay of Fermi-level pinning, increasing carrier effective mass, and decreasing achievable near-surface doping concentration. The best demonstrated ${\rho }_c$ values range from ~3 × 10⁻⁸ Ω·cm² for GaN epitaxially regrown contacts to ~8 × 10⁻⁵ Ω·cm² for direct AlN metallization. The transition from alloyed to regrown contacts in GaN—delivering two orders of magnitude improvement—is identified as the paradigm model for UWBG contact development, with β-Ga₂O₃ most immediately positioned to follow this trajectory. Key challenges include the absence of p-type doping in β-Ga₂O₃, near-complete Fermi-level pinning in AlN, and the unsolved shallow-donor problem in diamond. Recommendations for standardized ${\rho }_c$ measurement protocols and priority research directions are presented. Full article

The representations of a Buddha flanked on either side by Brahmā and Indra from Gandhāra have long been identified as the iconography of “Entreaty to Teach,” an episode in the Buddha’s life. After he obtained enlightenment, Śākyamuni had planned to enter nirvana immediately but Brahmā and Indra persuaded the Buddha to teach the Dharma for the sake of sentient beings. This subject is quite prominent in Gandhāran Buddhist art. It appears among a group of earliest anthropomorphic representations of the Buddha. It is one of the first events singled out from the Buddha’s life to be represented with the Buddha image; this event does not glorify the Buddha, but rather contaminates his imagery somewhat. In fact, pairing Brahmā and Indra on either side of a main figure became very common later in Gandhāran, art and it also appears in other iconographies. Were these images meant only to represent the narrative of “Entreaty to Teach?” This study reassesses this well-known iconography in both visual and textual traditions. Challenging current identification in the field, the author suggests the possibility that this iconography represents an iconic image of the Buddha that is meant to glorify the Buddha by subordinating the top two Brahmanic gods as his attendants. Full article

Background: Pediatric cancer is a highly stressful and potentially traumatic condition affecting physical health, emotional well-being, developmental trajectories, and family functioning. Hospitalization and intensive treatments often exacerbate emotional distress and symptom burden, negatively impacting quality of life (QoL). Integrating psychosocial and expressive interventions into pediatric oncology care is increasingly recognized as essential. Clay therapy is a multisensory, hands-on creative intervention that may promote emotional regulation, coping, and a sense of agency in hospitalized children. Objectives: This prospective pilot study evaluated the effects of clay therapy on emotional and physical well-being in pediatric oncology patients and explored potential indirect effects on caregivers’ emotional status. Methods: From December 2023 to December 2024, forty hospitalized children with onco-hematological diseases and one parent per patient were enrolled. Each child participated in a one-hour clay therapy workshop led by a professional ceramist. Emotional outcomes were assessed before (T0) and immediately after (T1) the intervention using the Visual Analog Scale (VAS) and the ArtsObs observational scale. Physical symptoms, including pain, fatigue, and nausea, were also evaluated. Results: Following clay therapy, children showed statistically significant improvements across all VAS emotional domains, independent of age and gender. ArtsObs assessments confirmed a significant increase in observed mood, with high levels of relaxation and engagement; female patients demonstrated greater mood improvement than males. Significant reductions in pain, fatigue, and nausea were also observed. Parents exhibited a significant improvement in mood following their child’s participation. Conclusions: These findings suggest that clay therapy is feasible and could be an effective supportive intervention in pediatric oncology, benefiting both emotional and physical well-being. Its integration into multidisciplinary, family-centered care models may enhance QoL during hospitalization and provide holistic support for children with cancer and their families. Full article

Knowledge Tracing, which estimates how students’ knowledge evolves during interactions with educational content, is a cornerstone of Intelligent Tutoring Systems. While deep learning models achieve superior predictive performance in this task, they lack interpretability, a limitation that is particularly critical in educational contexts. We introduce gTransformer, a new type of grounded Transformer model bridging deep learning performance with intrinsic interpretability through representational grounding. It adds theory-based parameters to input interaction sequences and uses attention mechanisms to transform them into latent representations. These are projected into enriched parameters that incorporate historical learning context while preserving semantics. Validation demonstrates: (1) structural encoding around theoretical concepts (probing selectivity $\Delta R^2>0.5$); (2) semantic alignment; and (3) functional alignment with quantified confidence. Results show that gTransformer achieves predictive performance competitive with state-of-the-art architectures while offering intrinsically interpretable predictions. The trade-off is characterised by a significant Area Under the Curve (AUC) gain over traditional theory-based models (+19.9%), with a minimal cost (3.9%) relative to non-interpretable configurations. Critically, gTransformer enables context-aware personalisation by differentiating students based on longitudinal learning trajectories rather than immediate responses, capturing patterns that traditional models cannot represent. This offers a practical path toward adaptive instruction driven by artificial intelligence that is both accurate and interpretable. Full article

Physical separation between family members arises not only from life choices such as education and employment, but also from health-related constraints that limit physical co-presence. This paper presents StepsConnect, a real-time step-sensing-based ambient display system that transforms personal walking data into dynamic digital art, providing low-effort and non-intrusive presence cues for family members living apart. The system continuously captures step data via smartphones and renders them as spatial and embodied visual cues embedded in everyday environments. We conducted a 90 min laboratory study with 15 young adult–parent dyads, in which young adults engaged in a simulated work session while viewing real-time visualizations of their parents’ step activity. Young adults’ perceived connectedness was measured using the Inclusion of Other in the Self (IOS) scale and complemented with semi-structured interviews, while parents’ walking data were logged to provide an objective behavioral reference. Quantitative results indicated modest and heterogeneous changes in IOS scores at the group level, with individual variability across participants. Qualitative findings suggested that step-based visualizations primarily functioned as ambient reminders and cues of presence, supporting momentary relational awareness while remaining calm and non-intrusive within the workspace context. Walking data exhibited large variation across dyads, providing objective context for participants’ subjective experience of presence, although connectedness was not simply proportional to activity magnitude. The findings suggest that aesthetic step-based ambient visualization primarily supports momentary relational awareness rather than immediate shifts in stable closeness. By clarifying this distinction, the study advances understanding of how sensing-based digital art may function as a complementary presence layer in intergenerational contexts. Full article

This paper presents a repeated cross-sectional longitudinal (trend) analysis of students’ self-perceived sustainability competence development across three waves surrounding participation in the Art Nouveau Path, a heritage-based mobile augmented reality game designed to foster sustainability competences, located in Aveiro, Portugal. In total, 1094 questionnaires were collected using a GreenComp-grounded instrument adapted from the GreenComp-based Questionnaire (GCQuest) to this context (25 items; 6-point Likert). Data were gathered at three stages: pre-intervention (S1-PRE; N = 221), immediately post-intervention (S2-POST; N = 439; n = 438 retained for scale scoring after applying a predefined completeness criterion), and follow-up (S3-FU; N = 434). Because responses were anonymous, waves were treated as independent samples rather than within-student trajectories. The Embodying Sustainability Values domain score and item-level response distributions were compared across waves using ordinal-appropriate non-parametric group comparisons, effect-size estimation, and descriptive threshold indicators. Results indicate an improvement from pre-intervention to post-intervention, followed by partial attenuation at follow-up while remaining above pre-intervention. Mean scores increased from 3.70 (S1-PRE) to 4.64 (S2-POST) and then stabilized at 4.13 (S3-FU). Findings, while exploratory, suggest that this heritage-based augmented reality game may have enhanced perceived sustainability competences. A structured program of follow-up activities is proposed to help sustain gains. Full article

Active involvement in creative activities, known as creative health, has been shown to enhance wellbeing, with museums serving as unique spaces for health promotion; however, visitors often require guidance to derive significant benefits from these institutions. This study, part of the larger ASBA (Anxiety, Stress, Brain-friendly museum Approach) project, evaluates the first phase of an intervention specifically focused on a Mindfulness protocol adapted to museum contexts. It has employed a single-group pre–post design with 79 healthy adults recruited from the non-clinical population. Participants were involved in a 15 min standardized mindfulness practice adapted from Mindfulness-Based Stress Reduction (MBSR) in either an art or science museum. State anxiety (SAI) and mood (VAS) were assessed at baseline and post-intervention, alongside personality traits (BFI-10) and interest measures to identify individual moderators of treatment response. The practice appeared to reduce state anxiety significantly in both settings, with large effect sizes. Specific moderators emerged: openness to experience predicted anxiety reduction in the art museum, whereas science interest predicted outcomes in the science setting. These findings suggest that brief, standardized mindfulness protocols implemented through the ASBA framework can provide promising immediate benefits for visitor wellbeing across diverse museum environments. Full article