Search Results (36,842)

Background: Early cardiopulmonary resuscitation (CPR) and timely use of automated external defibrillators (AEDs) are critical determinants of survival following out-of-hospital cardiac arrest (OHCA). University students enrolled in healthcare degree programs represent a strategic target population for the dissemination of basic life support and defibrillation (BLS-D) skills. However, evidence on their level of knowledge, attitudes, and perceived preparedness remains limited in Southern Italy. Methods: A cross-sectional observational study was conducted between mid-December 2025 and 15 January 2026 among undergraduate healthcare students at the Magna Graecia University of Catanzaro (Italy). Data were collected using a structured, self-administered questionnaire assessing socio-demographic characteristics, CPR/AED knowledge, attitudes, and perceived confidence. Composite knowledge scores were calculated and categorized as poor, sufficient, good, or excellent. Statistical analyses included chi-square tests, Cramér’s V, and Spearman’s rank correlation. Results: A total of 604 students were included (mean age 24.4 ± 6.7 years; 69.9% female), of whom 46.4% reported prior BLS-D training. Knowledge levels were heterogeneous: myocardial infarction was widely recognized as a cause of cardiac arrest (81.1%), whereas recognition of non-shockable rhythms, including asystole and pulseless electrical activity, remained low (<25%). Procedural knowledge, particularly regarding the chain of survival and chest compression rate, improved with academic year and prior BLS-D training. Conversely, ventilation skills and correct AED pad placement were consistently inadequate. Attitudes toward CPR were largely positive; however, perceived confidence in performing resuscitation was moderate to low, especially in complex scenarios. More than 80% of students expressed strong interest in further training and supported mandatory BLS-D education. Conclusions: Healthcare students demonstrated favorable attitudes toward CPR but insufficient and uneven knowledge, particularly in rhythm recognition, ventilation, and AED use. Academic progression and structured BLS-D training were associated with improved competencies, although critical gaps persisted. Integrating mandatory, hands-on BLS-D training with regular refresher sessions into healthcare curricula should enhance preparedness and potentially reduce OHCA-related mortality, especially in high-risk regions such as Calabria. Full article

This systematic integrative review evaluates the effectiveness of SGLT2 inhibitors in relation to improving glycaemic control, reducing cardiovascular events, and preserving renal function based on the latest published evidence. Search for publications referenced in PubMed, from January 2020 to January 2025, was conducted; 48 abstracts were reviewed, and 27 full-text articles were included for analysis—systematic reviews, meta-analyses, narrative reviews and comparative effectiveness studies. SGLT2 inhibitors are effective in reducing glucose levels, but the magnitude of reduction varies compared to other classes of antidiabetics. A noticeable reduction in the risk of major cardiovascular events, cardiovascular and all-cause mortality was reported, particularly compared to DPP-4 inhibitors and placebo. SGLT2 inhibitors demonstrated the most pronounced and consistent benefits in reducing hospitalisation for heart failure among all other evaluated classes. However, outcomes like myocardial infarction and stroke results were inconsistent. Renal outcomes consistently favoured SGLT2 inhibitors in reducing the risk of acute kidney injury, slowing chronic kidney disease and lowering the risk of end-stage kidney disease. SGLT2 inhibitors provide consistent glucose-lowering, cardiovascular and renal benefits. However, heterogeneity in study designs, patient populations, and treatment durations does not allow drawing definitive conclusions and highlights the need for future research focused on conducting well-designed trials with standardised methodology. Full article

Green innovation is a crucial aspect of an enterprise’s core competitiveness and long-term sustainable development, garnering significant attention from both academic scholars and industry practitioners. However, while existing research has primarily focused on green innovation at the organizational level, the mechanisms driving green innovation behaviors at the individual level have not been thoroughly explored in the literature. This study is grounded in the classic Job Demands–Resources (JD-R) theoretical framework and highlights the interplay between job demands (such as environmental ethics and corporate environmental strategies) and job resources (such as green human resource management practices and green transformational leadership). It also integrates individual-level characteristics, specifically green mindfulness and connectedness to nature, to construct a multidimensional interactive model aimed at uncovering the complex mechanisms driving employees’ green innovation. To achieve this, the study employs fuzzy-set qualitative comparative analysis (fsQCA). The findings suggest that no single condition is necessary for employee green innovation. However, connectedness to nature consistently appears across all core configurations, indicating a prominent “enabling” effect. This suggests that employee green innovation is an active and proactive form of environmentally responsible behavior, largely driven by individuals’ emotional affinity with nature. Additionally, connectedness to nature serves as a foundational source of intrinsic motivation for environmental awareness and acts as a catalyst across multiple pathways. Configurational analysis reveals an equifinal pattern, identifying three distinct motivational pathways: (1) Self-motivation Combined with Resource Support; (2) Self-motivation Combined with Job Demands; and (3) Triple Interaction of Demand, Resources, and Individuals. This study possesses both theoretical and practical significance in systematically examining green innovation behaviors at the individual level. Full article

System logs generated in IoT-based and sensor-driven cloud environments encode execution traces and complex relationships among services, functions, and data stores. In many IoT deployments, telemetry is pre-processed at the edge and then integrated into backend services (e.g., application servers and databases) for analytics and operations. During this integration, service executions record relational dependencies (e.g., function-to-data-store interactions) as operational logs (or aggregated statistics), which constitute key evidence for operating sensor-driven services. We therefore evaluate TLV using publicly reproducible backend execution logs as a representative backend model and discuss the generality and limitations of this choice. However, most existing retrieval-augmented generation (RAG) approaches remain document-centric, representing logs as flat textual chunks that fail to preserve execution flow and entity relationships, which are critical for diagnosing complex service execution pipelines in sensor-driven cloud backends. In this study, we propose Trace-LogVector (TLV), a relational log representation that transforms system logs into trace-level retrieval units while explicitly preserving execution order and entity interactions. TLV is constructed based on the Chunk as Relational Data (CARD) design principle, which represents execution flows using entity-centric multi-chunk structures rather than single aggregated text chunks. To evaluate the impact of relational log representation, we conduct controlled experiments comparing single-chunk and CARD-based multi-chunk TLV under identical embedding and retrieval settings. Retrieval performance is quantitatively assessed using Hit@5 and Mean Reciprocal Rank at 5 (MRR@5). Experimental results show that the proposed multi-chunk TLV achieves a Hit@5 of 1.000 and an MRR@5 of 0.900, consistently outperforming the single-chunk baseline across all evaluation queries. These findings demonstrate that preserving execution contexts and entity relationships as relational retrieval units is a key factor in improving RAG-based system log analysis for monitoring and diagnosing large-scale sensor networks and cloud systems. Full article

This study analyses the development of digital and artistic competence among pre-service language teachers within the framework of a teaching innovation project (2018–2024) at the University of Oviedo. It not only explores student teachers’ perceptions of the proposal’s pedagogical usefulness but also seeks to determine whether statistically significant differences exist between participating master and undergraduate students. The research adopts a mixed-methods approach: the qualitative component is based on the European DigCompEdu framework, while the quantitative component employed an ad hoc questionnaire analysed using SPSS (v.22), including descriptive analysis, Levene’s test to assess equality of variances and Student’s t-test to identify potential significant differences according to the master–undergraduate variable. The results indicate, on the one hand, that this initiative successfully strengthens five of the six areas defined in the European framework, and on the other, that there is an overall high level of satisfaction, reflected in the high scores obtained in the competences examined in this study: artistic, digital and pedagogical. These findings underscore the value of integrating innovative, video-based strategies into teacher education programmes to support the development of key competences required for 21st-century teaching. Full article

Background: Fibromyalgia (FM) and eating disorders (ED) represent distinct clinical entities traditionally managed within separate medical specialties, yet emerging evidence suggests significant comorbidity and potential shared pathophysiological mechanisms. Both conditions disproportionately affect women, involve complex multifactorial etiologies and substantially impair quality of life. Despite documented clinical overlaps, the mechanistic connections linking these conditions remain poorly characterized, and integrated treatment approaches are lacking. Objective: This narrative review examines the role of oxidative stress and nuclear factor erythroid 2-related factor 2 (Nrf2) pathway dysfunction as a unifying molecular mechanism connecting fibromyalgia and eating disorders, with emphasis on implications for integrated rehabilitation strategies. Methods: We synthesized current evidence on oxidative stress pathophysiology in fibromyalgia and eating disorders, focusing on Nrf2-Keap1 pathway function, clinical comorbidity patterns and rehabilitation interventions targeting antioxidant defense mechanisms. In PubMed, representative search strings included “(fibromyalgia [MeSH] OR fibromyalgia [Title/Abstract]) AND (“eating disorders” [MeSH] OR “anorexia nervosa” [MeSH] OR “bulimia nervosa” [MeSH])” and “fibromyalgia AND (“oxidative stress” OR Nrf2 OR “redox”)”. Articles in English published through December 2025 were considered, with additional records identified by manually screening reference lists. Results: Fibromyalgia patients exhibit elevated oxidative stress markers, impaired antioxidant enzyme function and compromised Nrf2 activity correlating with disease severity, with studies reporting approximately 30–50% reductions in coenzyme Q10 levels compared with healthy controls. Similarly, eating disorders demonstrate mitochondrial dysfunction and oxidative stress dysregulation, though patterns differ across eating disorder phenotypes. Nrf2 serves as the master regulator of cellular antioxidant defense, coordinating expression of over 500 genes involved in detoxification, cytoprotection, inflammation modulation and metabolic regulation. Evidence suggests Nrf2 activity is regulated by energy balance, potentially linking nutritional status with cellular stress responses. Rehabilitation interventions, including graduated exercise and nutritional optimization with Nrf2-activating foods (cruciferous vegetables, polyphenols, omega-3 fatty acids), offer mechanism-based therapeutic approaches through hormetic Nrf2 activation and direct Keap1 modification. Conclusions: Multidisciplinary rehabilitation programs integrating physical therapy, exercise prescription and nutritional strategies targeting Nrf2 activation offer evidence-based, mechanism-driven approaches to address shared oxidative stress pathophysiology. Nrf2 pathway dysfunction represents a promising and biologically plausible molecular target that may help to unify our understanding of fibromyalgia and eating disorders pending confirmation from prospective clinical studies in comorbid populations. Future research should prioritize prospective clinical trials testing Nrf2-targeted interventions in comorbid populations and collaborative patient-centered care models. Full article

Intelligence metrics based on benchmark performance or population norms are useful for measuring comparative ability within defined test environments, but they do not directly evaluate the structural coherence of an agent’s trajectory across time, domains, and perturbations. This article introduces Reflexive Signature Intelligence (RSI) as a bounded theoretical framework for addressing that different problem. RSI is developed within a causal-symmetric informational perspective in which intelligence is understood as the capacity of a system to maintain and restore alignment with a structurally constrained invariant without collapsing the open gradient of development. On this basis, the paper formulates the Principle of Bounded Subjectivity and the Prohibition of Finality as framework-level principles, arguing that intelligence should be assessed not as arrival at a completed end state but as the quality of an asymptotic trajectory. The framework is then operationalized on two coupled levels: a micro-level proposed as a future measurement program linked heuristically to resilience and prediction-error dynamics, and a macro-level expressed through five dimensions of structural integrity, including reflexive regulation, cross-domain integration, internal consistency, stabilization, and signature-setting. The article concludes by outlining implications for AI evaluation and alignment, with particular relevance for distinguishing full agents, partial systems, and human–AI composite configurations. Full article

Lipoprotein(a) [Lp(a)] is a low-density lipoprotein-like particle that contains a unique apolipoprotein(a) [apo(a)] component covalently bound to apolipoprotein B-100. Elevated levels of Lp(a) have been identified as a well-established and genetically determined risk factor for atherosclerotic cardiovascular disease, including coronary artery disease, stroke, and calcific aortic valve stenosis. In contrast to other lipids, Lp(a) concentrations are minimally influenced by lifestyle or traditional lipid-lowering therapies, emphasizing the necessity for novel treatment approaches. This narrative review summarizes current and emerging therapeutic strategies for reducing Lp(a) levels. Such strategies include traditional agents such as niacin and PCSK9 inhibitors, as well as innovative therapies such as antisense oligonucleotides, RNA interference-based molecules, and small-molecule inhibitors. The mechanisms of action of these agents, in addition to clinical trial data and their capacity to modify cardiovascular outcomes, are explored in further detail. Furthermore, the current status of clinical guidelines and the evolving role of Lp(a)-targeted therapies in cardiovascular risk stratification are reviewed. A particular emphasis is placed on therapies that are in the advanced stages of clinical development. These include late-phase outcome trials and orally administered agents, which have the potential to significantly impact future clinical practice. The integration of mechanistic data with ongoing and completed clinical studies has been undertaken in order to provide a comprehensive framework for understanding the therapeutic potential of Lp(a) in the context of cardiovascular prevention. Full article

A coupled non-destructive testing (NDT) workflow is proposed that integrates active infrared thermography (IRT) with laser-scanning-based reverse engineering (RE) to increase the reliability of detecting and interpreting damage in composite wind turbine blades across laboratory specimens and real components. IRT provides rapid, image-based qualitative localisation of potential anomalies, while 3D scan analysis supplies quantitative, geometry-aware verification and measurement of defect magnitude, reducing both false positives (design-related thermal signatures) and false negatives (weak thermal contrast). On polystyrene-filled profiles, IRT alone produced thermal anomalies unrelated to delamination; co-registered scan maps identified or ruled out local indentation, correctly attributing heat-flow patterns to internal design rather than damage. Outcome: the fused method disambiguates thermal indications and quantifies defect magnitude. On a vertical-axis wind turbine (VAWT) blade, the integration distinguished genuine geometric change from architectural effects under unknown internal structure and without CAD/reference scans, preventing false calls. For three horizontal-axis wind turbine (HAWT) blades, fleet-level scan comparison detected a significant tip deviation despite no clear local IRT anomalies, demonstrating complementary roles: scan = global quantitative homogeneity; and IRT = local qualitative verification. These findings operationalise thermal–geometric cross-validation and outline a path toward UAV-enabled inspections combining passive IRT and laser scanning for hard-to-access structures under real environmental conditions. Full article

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management. Full article

Urbanization significantly reshapes urban form, affecting the spatial and quantitative dynamics of urban land use under carbon constraints. However, the role of macro-scale urban form in guiding low-carbon urban expansion remains underexplored. Our study introduces an integrated Cellular Automata (CA) model to simulate urban land use patterns with regard to the low-carbon goal, focusing on urban form optimization. The model employs a top-down strategy to adjust future urban land demand by balancing urban development needs with carbon emission (CE) reduction targets. The adjusted demand is then used to optimize urban form parameters (i.e., the inverse S-shaped function) to predict future urban land patterns and allocate land increments within concentric rings. Subsequently, a bottom-up strategy incorporating carbon sequestration (CS) conservation is applied to refine urban land conversion. The CA model integrates a maximum probability transformation rule to allocate urban land efficiently. We used the model to simulate urban land use patterns under four scenarios (i.e., Low-carbon Urban Development Scenario (L-UDS), Top-up Urban Development Scenario (T-UDS), Bottom-up Urban Development Scenario (B-UDS), and inverse S-shaped constraint Urban Development Scenario (S-UDS)) for the Changsha–Zhuzhou–Xiangtan (CZX) urban agglomeration in 2035. Results show that the proposed model effectively reconciles the conflict between rapid urbanization and urban carbon management strategies, as evidenced by a 31.25% reduction in carbon emissions in the L-UDS and T-UDS relative to the S-UDS and B-UDS. Furthermore, urban form constraints promote the development of compact and dense urban structures, advancing sustainable urban development goals. This study not only proposes a simulation model capable of effectively promoting compact urban development at the theoretical level, but its findings also offer actionable policy insights for China to address urban sprawl and actively advance low-carbon urban development. Full article

The sustainable valorization of citrus processing by-products represents a key challenge for the food industry, aiming to reduce waste while recovering valuable bioactive compounds. In this study, a cloud point extraction strategy was developed using soy lecithin as a natural, food-grade surfactant to isolate phenolic antioxidants from orange juice industry residues. Response Surface Methodology was applied to two streams of orange juice by-products, to evaluate the combined effects of pH, NaCl concentration, and lecithin content on extraction efficiency, with total polyphenolic content, DPPH radical scavenging activity, and ferric reducing antioxidant power serving as response variables. Partial Least Squares (PLS) analysis was additionally employed to integrate all antioxidant responses and identify a multivariate optimum. The optimized conditions (pH 3.4, 12% NaCl, 11% lecithin) enabled maximal recovery of antioxidant constituents, highlighting the effectiveness of lecithin-based micellar systems. To assess practical applicability, the optimized extract from the oil emulsion residue (Stream A) was incorporated into tsipouro, a traditional Greek distillate, and its stability was monitored under controlled light and temperature conditions for 30 days at three concentration levels. Results demonstrated that both environmental factors significantly influenced antioxidant retention and physical stability, underscoring the importance of formulation design. Specifically, high gel concentration at 2% w/v, low temperature at 20 °C and light exposure provided the highest overall desirability for TPC, FRAP, and DPPH responses. Overall, this work introduces a green, scalable, and food-compatible extraction approach that not only supports circular economy principles but also opens new opportunities for the development of functional alcoholic beverages enriched with natural antioxidants. Full article

Background/Objectives: Accurate preoperative discrimination between women with ovarian pathology and healthy controls, as well as between benign and malignant ovarian tumors, remains challenging. This study aimed to evaluate the usefulness of osteopontin and galectin-7 on the diagnosis of ovarian tumors. Methods: This prospective single-center case–control study was conducted at the Third Department of Obstetrics & Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece, between 2018 and 2024. Preoperative serum levels of osteopontin, galectin-7, and established tumor markers (CA-125, CA19-9, CA15-3, CEA, AFP) were analyzed. Biomarker distributions were compared using non-parametric tests. Associations with clinical variables were explored using correlation analyses. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess diagnostic performance. Results: The study population included 116 women: 52 healthy controls, 45 patients with benign ovarian tumors, and 19 patients with malignant ovarian tumors. Serum osteopontin and galectin-7 levels did not differ significantly between control and study group (p = 0.562 and p = 0.138, respectively), nor between benign and malignant tumors (p = 0.784 and p = 0.140, respectively). Osteopontin showed no discriminatory ability (AUC = 0.47), while galectin-7 demonstrated weak discrimination (AUC = 0.63). A combined model yielded modest improvement (AUC = 0.69), remaining below clinically meaningful thresholds. CA-125 was the only biomarker significantly associated with malignancy (OR = 1.03, p = 0.038). Galectin-7 levels were higher in premenopausal women and inversely correlated with age, suggesting demographic rather than malignant influence. Conclusion: Despite strong biological relevance, circulating osteopontin and galectin-7 did not provide meaningful diagnostic discrimination between women with ovarian pathology and healthy controls or between benign and malignant ovarian tumors. CA-125 remained the most informative serum marker in this setting. Future efforts should focus on multi-marker strategies integrated with imaging and clinical assessment. Full article

The electricity supply system faces major challenges. The physical and social vulnerability of the monoculture of hierarchical, centralized systems urgently requires radical transformation of their organizational structures as well as their infrastructures. These transformations to low carbon are often characterized as ‘decentralization’. However, decentralization is a process that only signifies a move away from centralized models. This does not necessarily result in a decentralized architecture, but rather a model in which the dominance of ‘commercial private’ combined with ‘monopolistic public’ is replaced by cooperation and community. The research question is: what will be the design of future electricity grids after the transformation? The integration of distributed renewable resources and the growing need for resilience requires great diversity and flexibility from socio-technical smart grids. These involve digitization, enabling the transformation of power grids into networks of clustered, self-healing microgrids with distributed energy systems: generation, storage, transmission, demand response, and internal energy management. Several fundamentals of Common Pool Resources theory (Ostrom) on the analysis of sustainable management of natural resources are reviewed on their relevance: the Socio-Ecological System framework, distinct property regimes, the Polycentricity concept, and the Institutional Analysis and Development (IAD) framework. The transformation leads to ‘distributed’ rather than ’decentralized’ models. Governance no longer takes place from a single control point, but from many, spread across multiple levels, similar to ecosystems. End users play a key role and become partly coproducing prosumers. Governance is polycentric rather than decentral. The IAD provides as its most important condition that, at the legislative level, there must be minimum recognition of the right of ‘renewable energy communities’ to organize themselves as microgrids. This is immediately the biggest social acceptance challenge, as the current monoculture incorporates several lock-ins: incumbent powerful actors, centralized hierarchical control legislation, and obstructive market conditions, including taxing systems. Full article

Against the background of global climate change and intensified human activities, the Black Soil Region of Northeast China (BSRNC)—an ecologically fragile zone and critical grain-producing area—faces mounting pressures on ecosystem stability, productivity sustainability, and black soil conservation. Clarifying the spatiotemporal evolution characteristics of vegetation net primary productivity (NPP) and its associative patterns is crucial for ecological protection and sustainable land management in this region. Based on remote sensing, meteorological, topographic, soil and human activity data, this study employed the improved Carnegie–Ames–Stanford Approach (CASA) model to quantify vegetation NPP—an analytical approach that integrates the CASA model with tree-based machine learning and SHapley Additive exPlanations (SHAP) interpretation. By further combining multiple spatial analysis methods, it characterizes the spatiotemporal dynamics of NPP in the black soil region and innovatively compares seven machine learning algorithms to select the optimal Light Gradient Boosting Machine (LightGBM) model for quantifying the contributions of drivers in this region with high spatial heterogeneity. The results showed that the average annual vegetation NPP in the BSRNC was 301.18 g C·m⁻², exhibiting a fluctuating upward trend at a rate of 1.55 g C·m⁻²·a⁻¹ over the 24-year period. Spatially, NPP displayed significant heterogeneity, climbing gradually from the region’s southwest to its northeast quadrant, with over 90% of the territory showing an upward trajectory. Overall NPP reached a high stability level, though the western and southern regions faced higher degradation risks, and the entire region presented a weak anti-persistent trend. Precipitation was the dominant factor associated with NPP variations, followed by soil moisture, while soil pH had the smallest correlative contribution (0.38). Land-use changes were positively associated with NPP growth, and the interaction of multiple factors showed a significant associative pattern with NPP variations. This study clarifies the spatiotemporal patterns and associative patterns of vegetation NPP in the BSRNC with a 24-year-long time series, and its incremental findings on the coupling of land-use change and multi-factor interaction provide a targeted scientific basis for ecological protection, restoration policies and sustainable management of black soil resources. Full article