Search Results (16,023)

Background: Despite significant advances in genetics, immunology, and endometrial research, the underlying cause of nearly half of recurrent pregnancy loss (RPL) cases remains unknown. This highlights the limitations of conventional diagnostic approaches and underscores the need for methods that can detect complex, subtle biological patterns. Objectives: To summarize and critically assess how artificial intelligence (AI) is changing our knowledge of, ability to predict, and future therapeutic management of RPL, with a focus on machine learning (ML) approaches that identify latent biological pathways and multifactorial contributors to pregnancy loss. Methods: This narrative review summarizes contemporary research on AI applications in reproductive medicine. Research using imaging, proteomic, genomic, clinical, and multi-omics information to create predictive or mechanistic models associated with RPL provided evidence. Results: AI-based approaches are increasingly demonstrating the ability to detect complex interactions among environmental, immunological, biochemical, and genetic factors associated with RPL. ML and deep learning (DL) models enhance prognostic accuracy, identify novel candidate biomarkers, and provide insights into the systemic and molecular mechanisms underlying pregnancy loss. Integrating heterogeneous data through AI supports the development of personalized reproductive profiles and can improve prediction and counseling. Conclusions: AI has the potential to improve both personalized prediction and mechanistic understanding of RPL. However, clinical translation is currently hampered by a number of important issues, including small and diverse datasets, conflicting diagnostic definitions, limited external validation, and a lack of prospective clinical trials. To responsibly integrate AI tools into reproductive care, these limitations must be addressed. Full article

This review presents a focused assessment of the rapidly expanding field of gelatin-based eutectogels and identifies the gaps in current literature that justify this examination. Research on deep eutectic solvents (DESs and NADES) has advanced quickly, yet there is still no integrated view of how these solvent systems influence adhesion in gelatin-based gels. Eutectogels are soft materials formed by gelling DESs or NADES with biopolymers. Gelatin is widely used because it is biocompatible, biodegradable, and readily available. We provide a clear overview of the chemistry of DESs and NADES and describe how gelatin forms networks in these media. The review summarizes established knowledge on adhesion, highlighting the contributions of polymer network density, interfacial hydrogen bonding, and solvent mobility. New perspectives are introduced on how these factors interact to control adhesion strength, toughness, and reversibility. A key topic is the role of hydrogen bond donors (HBDs) and acceptors (HBAs). They define the hydrogen bonding environment of the solvent and represent an underexplored way to tune mechanical and adhesive behavior. Examples such as moisture-resistant adhesion and temperature-responsive bonding show why these systems offer unique and adjustable properties. The review concludes by outlining major challenges, including the lack of standardized adhesion tests and constraints in scalable production, and identifying directions for future work. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

This study developed and evaluated an adaptive e-learning environment for selected number theory topics using a mixed-methods research design, conducted over an eleven-month period across secondary and early tertiary education contexts. The evaluation focused on three primary outcome domains: (1) learning-related outcomes (problem-solving accuracy and task success rate), (2) learner engagement and activity indicators (daily logins and tasks completed per day), and (3) system usability, assessed according to Jakob Nielsen’s usability dimensions. Quantitative data were collected through student and teacher questionnaires (N = 264 students; N = 52 teachers) and large-scale logfile analytics comprising more than 825,000 recorded system interactions. Qualitative feedback from students and teachers complemented the quantitative analyses. The results indicate statistically significant increases in learner activity, task completion rates, and problem-solving success following the introduction of the adaptive system, as demonstrated by inferential statistical analyses with confidence intervals. Post-use evaluations further indicated high levels of learner motivation and self-confidence, along with positive perceptions of system usability. Teachers evaluated the system positively in terms of learnability, efficiency, and instructional integration. Logfile analyses also revealed sustained growth in daily engagement and task success over time. Overall, the findings suggest that adaptive e-learning environments can effectively support engagement, usability, and learning-related performance in number theory education, although further research is required to examine the sustainability of learning-related outcomes over extended periods and to further refine error-handling mechanisms. Full article

To align with disaster monitoring and sustainable risk assessment, the low-carbon transition of fisheries necessitates comprehensive carbon emission management throughout the supply chain. As China advances supply-side structural reform, transitioning from traditional to low-carbon fisheries is vital for the green development of the industry and its associated sectors. This study employs input–output models and LMDI decomposition to examine the trends and drivers of embodied carbon emissions within China’s fishery production system from 2010 to 2019. By constructing a cross-sectoral full-emission accounting system, the research calculates total direct and indirect emissions, exploring how accounting scopes influence regional responsibility and reduction strategies. Empirical results indicate that while China’s aquatic trade and processing have steadily developed, the sector remains dominated by low-value-added primary products. This structure highlights vast potential for deep processing development amidst shifting global dietary habits. Factor decomposition reveals that economic and technological development are the primary drivers of carbon emissions. Notably, technological progress within fisheries emerges as the most significant factor, playing a pivotal role in both driving and potentially mitigating emissions. Consequently, to effectively lower carbon intensity, the study concludes that restructuring the fishery industry is crucial. Promoting low-carbon development and enhancing the R&D of green technologies are essential strategies to navigate the dual challenges of industrial upgrading and environmental protection. Full article

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility. Full article

Ceramic-on-ceramic (CoC) bearings are widely used in total hip arthroplasty due to their extremely low wear rate, excellent chemical stability, and good biocompatibility. They are considered one of the most reliable long-term friction bearing systems. Although frictional instability, lubrication regime transitions, and microstructural damage mechanisms have been widely reported at the experimental and retrieval-analysis levels, current clinical evidence, limited by follow-up duration and event incidence, has not demonstrated a definitive negative impact on the clinical performance of fourth-generation ceramic components, including BIOLOX® delta. Data from national arthroplasty registries consistently demonstrate excellent survivorship and low complication rates for 4th-generation ceramics in both hard-on-soft and hard-on-hard configurations. The most reported causes for revision, such as infection, dislocation, aseptic loosening, and periprosthetic fracture, are not primarily associated with ceramic-related complications, such as ceramic fracture, excessive wear, squeaking, and revision, related to bearing failure; however, these mechanisms remain highly relevant for the design and evaluation of emerging ceramic materials and next-generation implant systems, where inadequate control may potentially impact long-term clinical performance. This review summarizes recent advances in the tribological research of CoC artificial joints, focusing on clinical tribological challenges, material composition and surface characteristics, lubrication mechanisms, wear and microdamage evolution, and third-body effects. Recent progress in ceramic toughening strategies, surface engineering, biomimetic lubrication simulation, and structural optimization is also discussed. Finally, future research directions are outlined to support the performance optimization and long-term reliability assessment of CoC artificial joint systems. Full article

Volatile organic compounds (VOCs) produced by Medicinal Aromatic Plants (MAPs) are bioactive signaling molecules that play key roles in plant defense, acting against pathogens and triggering resistance responses. Intercropping with VOC-emitting MAPs can therefore enhance disease resistance. This study investigated VOCs emitted by sage (Salvia officinalis) as potential resistance inducers in grapevine (Vitis vinifera) against Plasmopara viticola, the causal agent of downy mildew, under consociated growth conditions. Sage and grapevine plants were co-grown in an airtight box system for 24 or 48 h, after which grape leaves were inoculated with P. viticola. Disease assessments were integrated with grapevine leaf metabolic profiling to evaluate responses to VOC exposure and pathogen infection. Untargeted and targeted metabolomic analysis revealed that sage VOCs consistently reprogrammed grapevine secondary metabolism, without substantial differences between 24 and 48 h exposures. Lipids, phenylpropanoids, and terpenoids were markedly accumulated following VOC exposure and persisted following inoculation. Correspondingly, leaves pre-exposed to sage VOCs exhibited a significant reduction in disease susceptibility. Overall, our results suggest that exposure to sage VOCs induces signaling and metabolic reprogramming in grapevine. Further research should elucidate how grapevines perceive and integrate these signals, as well as the broader processes underlying MAP VOC-induced defense, and evaluate their translation into sustainable viticultural practices. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

Background: Longitudinal studies demonstrate an association between social media use and anxiety. However, the mechanism of this association in terms of emotional support is not completely understood. Methods: We used survey data among a national sample of 2403 individuals aged 18–30. Primary measures included the 4-item Patient-Reported Outcome Measurement Information System (PROMIS) scale to assess anxiety, self-reported emotional support derived from social media (SMES), and the 10-item Big Five Inventory (BFI-10) to determine personality structure. We performed factorial analysis of variance (ANOVA) and multiple regression analyses to examine the associations among these variables while controlling for age and sex. Results: SMES was associated with decreased anxiety. These associations were more pronounced among females. Personality traits of high openness to experience, high extraversion, high agreeableness, and low conscientiousness were associated with increased SMES. Limitations: Due to the cross-sectional research design and observation data, causal relationship could not be established. Conclusions: Emotional support derived from social media (SMES) may be linked to reduced anxiety, especially among females. SMES may also be linked with specific personality characteristics. Future research should investigate these associations longitudinally. Full article

Surfactants are commonly employed in cleaning, cosmetic, and pharmaceutical formulations due to their ability to lower surface tension and facilitate the formation of emulsions, foams, and dispersions. Recent research highlights the advantages of synergistic interactions between anionic and nonionic surfactants to improve overall performance. In this study, the physicochemical properties and performance of binary mixtures of the anionic surfactant sodium lauryl sulfate (SLS) and the amphoteric surfactant lauryl dimethyl amine oxide (LDAO) at varying ratios (100% SLS, 90:10, 80:20, 70:30, 60:40, and 50:50) were investigated. Key parameters analysed included critical micelle concentration (CMC), surface tension (γ), foam volume, and potential irritability, assessed via the Zein test. The results revealed a clear synergistic effect between SLS and LDAO: all mixtures showed reduced CMC and minimum surface tension compared to the individual surfactants, while exhibiting enhanced foam volume and stability. Regarding irritability, increasing LDAO content consistently led to decreased protein denaturation, indicating lower irritancy levels. Furthermore, the results obtained in the Zein test confirmed that mixtures induced less protein denaturation than the sum of their individual surfactant components, with formulations ranging from moderately to non-irritating. The results obtained indicate that the more stable mixed micelle systems (SLS + LDAO) might improve the performance of cleaning formulations (γ, CMC, foam) while reducing the irritability. Full article

Laying hens require lighting for proper development and reproduction. There is limited research on the effects that lighting types have on birds’ welfare quality. A novel lighting source, Pulsed Alternating Wavelength System (PAWS), is being evaluated in the industry that claims to improve birds’ growth rate, decrease age at first egg, and decrease aggressive and nervous behaviors. Understanding how PAWS effects hen’s welfare, both physically and physiologically, is critical if this technology is to be adopted by industry. The project evaluated the effects of two PAWS lighting recipes on neurotransmitter turnover and welfare quality of commercial, conventionally caged laying hens. Three flocks of White leghorn hens (control [fluorescent lights] and two PAWS flocks [PAWS1 and PAWS2]) were sampled from 22 to 70 weeks of age, depending on the flock. The physical welfare of 50 hens per flock and neurotransmitter turnover of 10 hens per flock were assessed at each timepoint. The majority of welfare quality parameters were influenced by age as opposed to lighting type. No differences in dopamine turnover were observed. The hens housed under PAWS1 had reduced serotonin turnover, thus increased serotonin activity, and PAWS2 hens had improved keel bone damage scores; both indicative of improved welfare compared to control hens. The novel lighting may be beneficial to layer welfare, which may lead to increased longevity and productivity. Implementation in cage-free housing should be explored to delve into potential behavioral differences that could further influence welfare outcomes. Full article

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m² near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance. Full article

Objectives: Clinical decision-support systems are computer-based tools to improve healthcare decision-making. However, their effectiveness depends on being positively perceived and well understood by healthcare professionals. Qualitative research is particularly valuable for exploring related behaviors and attitudes. This study aims to explore experiences of family physicians and nurses concerning the visualization, utility and understanding of the non-valvular atrial fibrillation clinical decision-support system (CDS-NVAF) tool in primary care in Catalonia, Spain. Methods: We performed a qualitative study, taking a pragmatic utilitarian approach, comprising focus groups with healthcare professionals from primary care centers in the intervention arm of the CDS-NVAF tool randomized clinical trial. A thematic content analysis was performed. Results: Thirty-three healthcare professionals participated in three focus groups. We identified three key themes: (1) barriers to tool adherence, encompassing problems related to understanding the CDS-NVAF tool, alert fatigue, and workload; (2) using the CDS-NVAF tool: differences in interpretations of Time in Therapeutic Range (TTR) assessments, and the value of TTR for assessing patient risk; (3) participants’ suggestions: improvements in workflow, technical aspects, and training in non-valvular atrial fibrillation management. Conclusions: Healthcare professionals endorsed a clinical decision-support system for managing oral anticoagulation in non-valvular atrial fibrillation patients in primary care. However, they emphasized the view that the CDS-NVAF requires technical changes related to its visualization and better integration in their workflow, as well as continuing training to reinforce their theoretical and practical knowledge for better TTR interpretation. Full article