Search Results (27,371)

Critical thinking is essential for managing socio-scientific issues (SSIs) and can be enacted through informal and rational reasoning for decision-making. Addressing SSIs from a systemic view, such as the One Health (OH) view, can improve their comprehension. This study explores how a group of 9th graders engaged in the practice of critical thinking in a teaching sequence that included different SSIs (antibiotic resistance, microplastic pollution, and avian flu) from an OH vision. The research questions are: (1) What is the ability of students to develop a critical position in the evaluation of claims related to SSI with an OH view? (2) What are the interactions between students’ critical positions and OH views expressed by them in different SSI contexts? Written answers from two activities at different moments of the sequence were analyzed qualitatively. Most students did not develop a critical position in the beginning; however, they did so at the end. No interaction was observed between the ability to develop a critical position and the OH view at the beginning. Students with a critical position at the end showed the highest levels of OH view. These results highlight the need to further investigate the relationships between critical positions and OH views. Full article

Upper extremity (UE) pain and pathology are prevalent among manual wheelchair users (MWUs) due to repetitive loading demands, highlighting the need for tools to identify high-risk tasks and inform injury prevention. This study investigated the feasibility of classifying activity intensity for wheelchair-related tasks using wearable sensors and supervised machine learning. Twenty-four MWUs with chronic spinal cord injury completed a standardized mobility course and simulated activities of daily living while UE electromyography (EMG) and inertial measurement unit (IMU) data were collected. Signals segmented into 3, 5, and 10 s windows, and time- and frequency-domain features were extracted and labeled as low, moderate, or high intensity. Multiple classification algorithms were evaluated using subject-dependent and subject-independent cross-validation, and dimensionality reduction was explored to assess class separability. Subject-dependent analyses demonstrated performance above chance but below 75% accuracy, with decision tree models demonstrating superior performance, particularly when trained on data segmented into 5 s windows. IMU features outperformed EMG features, but combining signal types enhanced performance. Subject-independent analyses revealed similar overall accuracy across signal types, but decreased high-intensity classification for EMG data, indicating subject dependency. Findings support the potential of wearable sensor-based machine learning with population-specific findings for activity intensity classification in MWUs, while highlighting challenges related to inter-subject variability for injury risk prediction. Full article

In the academic year 2022/2023, an orientation course addressed to high school students was proposed at Politecnico di Milano. The course was conducted using active methodologies referring to the Problem-Based Learning pedagogical framework. Most of the time was dedicated to an open-ended style physics laboratory in which students worked in small groups exploring different areas of physics. The main aim of the course was to foster science self-efficacy and motivation, in order to enable students to prepare themselves for passing the test required to enrol in any engineering programme at Politecnico di Milano University. To investigate the effectiveness of the course, a statistical analysis of students’ attendance and students’ results on the test was performed. The results of the analysis show that students who attended the course had a slightly better improvement in their test scores compared to those who did not attend. The impact of the course seems to be more effective for female students. The results confirm the validity of active and open-ended activities to increase scholastic performance and to enable students in autonomous preparation. Using these strategies in an orientation course can help students make more informed choices about the university pathway best suited to them, thereby reducing issues related to student dropout. Full article

Oxidative stress is a critical pathophysiological factor in sepsis. Ursodeoxycholic acid (UDCA), a bile acid with anti-inflammatory, antioxidant, and anti-apoptotic properties, may protect against lipopolysaccharide (LPS)-induced myocardial injury. In an experimental study, 32 male Wistar rats were randomly assigned to four groups: control, LPS, UDCA, and UDCA + LPS. UDCA was administered orally for 10 days prior to LPS-induced endotoxemia. Serum levels of high-sensitive troponin I (hsTnI), homocysteine, and oxidative stress markers were measured, and immunohistochemistry and immunofluorescence were used to assess inflammation (nuclear factor kappa B, NF-κB), apoptosis (caspase 3), and signaling pathways related to protein kinase B (Akt)/NF-κB and silent information regulator 1 (SIRT1)/nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1). UDCA pretreatment significantly reduced myocardial pathological changes, serum hsTnI, homocysteine, and total oxidative stress compared with LPS alone. It enhanced catalase (CAT) activity and glutathione (GSH) levels while lowering thiobarbituric acid reactive substances (TBARS) and nitrite concentrations in cardiac tissue. UDCA modulated cellular signaling by decreasing Akt phosphorylation and activating the SIRT1/Nrf2/HO-1 pathway. These results indicate that UDCA protects the heart from LPS-induced damage by reducing oxidative stress, inflammation, and apoptosis. UDCA modulates cellular signaling by decreasing pro-inflammatory pathways and activating anti-inflammatory pathways associated with SIRT1/Nrf2/HO-1 signaling, emphasizing its key role in myocardial protection during sepsis. Full article

Agro-industrial waste impacts populations worldwide. Food waste, in turn, is a major source of complex lipids, carbohydrates, and other substances. Therefore, it is crucial to convert food waste into products that reduce environmental problems. Enzymatic hydrolysis has advantages over chemical hydrolysis. Examples include the enzymatic hydrolysis of starch by α-amylase and the hydrolysis of inulin by inulinase, which occur under milder environmental and temperature conditions than acid hydrolysis of starch or inulin. Despite these milder temperature conditions, during substate hydrolysis, enzyme deactivation occurs under exposure to temperature. As temperature increases above T_opt (which maximizes catalytic activity), enzyme deactivation becomes more pronounced, leading to a decrease in enzyme activity. Therefore, determining the rate constant of deactivation k_d, during biotransformation is an important aspect in understanding enzyme kinetics. Most experimental studies focus on changes in enzyme activity with time and temperature. However, enzyme deactivation also occurs during enzymatic reactions conducted at different temperatures, and this process is characterized by specific deactivation parameters. The study is to present the rate constants of deactivation k_d, for selected biotransformation processes. The selected biotransformation processes are hydrolysis of olive oil by lipase, hydrolysis of inulin by inulinase, and hydrolysis of starch by α-amylase. Given the widespread use of enzymes in industry, the information on enzyme deactivation presented in this study can be used by engineers involved in modeling and optimizing enzymatic processes. This knowledge is also essential for the effective and sustainable use of enzymes in industrial applications. It is important to emphasize that the deactivation parameters discussed in this study also carry significant economic, social, and environmental implications. Full article

Engagement is essential in informal learning contexts, as it fosters meaningful learning, personal relevance, and sustained motivation. However, engagement is a complex construct that requires diverse methodological approaches for accurate assessment. This study empirically examines a multi-method approach, combining questionnaires, log file analyses, and observations, in the context of a tablet-based educational program developed for an exhibition on mobility and traffic. A total of 307 tenth-grade students from 21 classes at twelve state high schools participated in self-regulated learning activities during the museum visit. Findings reveal that each method offers distinct insights: questionnaires capture students’ self-reported engagement, log files track behavioral patterns, and observations provide qualitative evidence of interaction quality. Each method provides valuable, specific insights into student engagement. Thus, integrating multiple approaches yields a more comprehensive understanding of engagement. These results underscore the importance of methodological pluralism and critical reflection when interpreting research findings from different methodological sources in informal learning environments. Full article

Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit damage patterns, human perception naturally integrates both types of information into a holistic risk judgment. This study presents an exploratory investigation into the neural signatures underlying this integrated judgment process using electroencephalography. A modified paradigm was employed to probe the cognitive dynamics of risk evaluation in participants with civil engineering backgrounds. Although participants were instructed only to identify damaged buildings without explicit severity grading, event-related potential analysis revealed systematic, graded neural responses that scaled with damage severity. This suggests that the brain encodes damage-related information not as a binary state but as a continuous spectrum of perceived risk, implicitly processing severity, even in the absence of explicit instructions. Furthermore, single-trial analysis demonstrated that time-domain features contain robust discriminative information, verifying the feasibility of decoding these latent judgments from brain activity. These findings provide a physiological basis for developing future cognition-informed algorithms and human-in-the-loop frameworks, bridging the semantic gap to enhance the reliability of automated disaster assessment. Full article

Glioblastoma (GBM) persists as one of the greatest challenges in the treatment of human cancer, despite extensive efforts to leverage the therapeutic potential of immunotherapy. While checkpoint blockade and other forms of immunotherapy have revolutionized the treatment of various cancers, their therapeutic efficacy in GBM has been hindered by the profound immunosuppressive environment, spatial heterogeneity, and dynamic immune metabolic challenges associated with the tumor microenvironment. In this review, we will synthesize recent advances and insights to develop a next-generation framework for GBM immunotherapy based on systems biology approaches to understanding the complex interplay between GBM and the immune system, as opposed to single-axis approaches to immune activation and modulation. We will discuss how the functional competence of the interferon system, myeloid antigen presentation status, T-cell clone status, spatial organization of the immune microenvironment, and resource competition between GBM and the immune system dictate therapeutic responsiveness. Furthermore, the current paper elucidates how recent advances in spatial transcriptomics, single-cell analysis, and high-parameter imaging enable us to understand how immune phenotype status varies across GBM regions and treatment status, and how this information can be used to develop predictive and pharmacodynamic biomarkers of therapeutic efficacy and failure. We will then discuss how these advances form the basis for rational combination approaches to GBM immunotherapy, which involve the integration of checkpoint blockade with metabolic reprogramming, myeloid modulation, and interferon system reactivation, and how artificial intelligence-based analytics and adaptive clinical trial design can guide the development of biomarker-based therapeutic selection approaches. Full article

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to 2023, we apply several established statistical techniques—including unit root tests, variance ratio analysis, Hurst exponent estimation, and GARCH modeling—to quantify the presence and strength of mean reversion in volatility. To assess the accuracy and practical usability of volatility metrics for option valuation, we compare realized volatility, GARCH-based forecasts, range-based estimators, and widely used implied volatility measures such as the VIX and daily implied volatility averages, benchmarking each against contract-specific implied volatility. The results indicate that more than 65% of the analyzed tickers exhibit statistically significant mean-reverting behavior, and that the 30-day average implied volatility consistently provides the most reliable predictive performance among the tested metrics, while range-based estimators perform poorly when applied to end-of-day data. Finally, backtests of six delta-neutral option strategies informed by these findings did not yield consistent profitability or statistically significant outperformance, suggesting that although volatility mean reversion is measurable, its direct application to systematic trading remains challenging. Full article

Background: Dentists constitute one of the most heavily burdened groups of healthcare professionals, experiencing high levels of musculoskeletal disorders, occupational stress, burnout, and diminished quality of life. Although extensive literature addresses these issues, no study has directly examined biological age or epigenetic markers of aging in this population. This narrative review, informed by systematic methodological principles, seeks to fill this gap by connecting established occupational stressors with contemporary concepts of biological aging and chronomedicine, ultimately proposing a preventive well-being framework specifically for dentists. Methods: A narrative review informed by systematic methodology was conducted following PRISMA 2020 guidelines. Searches in PubMed, Scopus, and the Cochrane Library (2015–2025) used combined keywords and MeSH terms related to lifestyle factors, occupational stress, musculoskeletal disorders, quality of life, and wellness among dentists. Of the 943 records identified, 15 met the inclusion criteria and were assessed for outcomes, methodological quality, and relevant risk factors. Results: The included studies consistently indicated a significant occupational burden, with musculoskeletal pain, emotional exhaustion, anxiety, and depersonalization as frequent findings. Quality of life was generally moderate to low, especially regarding mental health. Lifestyle patterns were characterized by inadequate sleep, limited physical activity, irregular eating habits, and insufficient recovery. These conditions-chronic stress, poor sleep, inactivity, and suboptimal nutrition-are recognized accelerators of biological aging, implying that the professional demands of dentistry may adversely influence the biological clock. Although none of the studies measured biological age directly, the collective evidence underscores the need for preventive strategies informed by chronomedicine. Conclusions: This review highlights a critical gap in the dental literature: the complete absence of biological-age assessment in a professional population exposed to multiple aging accelerators. Integrating occupational health data with modern concepts of biological aging and chronomedicine, the study proposes a targeted preventive framework to regulate biological rhythms, reduce cumulative biological deterioration, and improve the long-term quality of life and professional sustainability of dentists. Full article

This article develops the analytical concept of post-liturgical female rituality to examine informal religious practices created by Western Ukrainian female labor migrants in Israel. Drawing on approaches that conceptualize ritual as flexible, embodied, and processual, it focuses on women’s ritual activities that take place in close temporal and symbolic proximity to official church liturgy while remaining outside canonical frameworks. Rather than directly challenging institutional religion, these practices extend and reinterpret patriarchal liturgy through gendered forms of ritual engagement. The analysis is based on qualitative research among Ukrainian Greek Catholic women in Israel, including 27 in-depth interviews, participant observation, and digital ethnography. The findings highlight three interconnected dimensions: collective gatherings following church services; post-liturgical practices involving food, singing, and embodied performance; and national-religious rituals expressing emotional belonging to Ukraine in the context of war. The article argues that post-liturgical female rituals constitute a distinct form of women’s religious agency that operates within institutional Christianity while reworking its meanings, contributing to feminist scholarship on ritual, migration, and war. Full article

How does the complex, adaptive, and autonomous organization of life emerge from the laws of physics and information? This review argues that the answer lies in a convergent set of universal organizational principles that constitute a physical and informational grammar of the living. Living systems are dissipative structures that achieve organizational closure—materially and energetically open, yet causally closed—thereby attaining genuine autonomy and agency. Their architecture exhibits fractal and modular scaling laws that maximize energy flow, robustness, and evolvability under universal physical constraints. Critically, organisms operate at critical transitions—zones of controlled instability where fluctuations amplify information processing, transforming noise into adaptive signal. This self-organized criticality enables functional degeneracy, relational redundancy, and evolutionary antifragility. Cognition emerges as a distributed process of active inference, operating through a predictive–corrective cycle that integrates perception, action, and learning under the Free Energy Principle. From molecular networks to ecosystems, the same physico-informational grammars unfold recursively, revealing a deep organizational holography: the principles of organization are replicated across scales. Evolution under the Law of Increasing Functional Information is not random drift, but a directional expansion of functional complexity—a thermodynamic gradient towards greater agency. This synthesis challenges biological exceptionalism: the trajectory from thermodynamics to cognition is continuous, physically constrained, and potentially inevitable. Life does not violate physical laws—it fulfills them in regimes of high informational complexity, instantiating fundamental principles in self-organized architectures capable of prediction, memory, and purpose. The objective of this work is to articulate how the synthesis of these principles not only unifies physics and biology, but also illuminates the profound continuity between thermodynamics, chemistry, informational constraints, organization, and the mind. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

This study investigates how parents and students interpret and form continued engagement intentions with a radar visualisation tool designed to present multi-subject academic performance. While data visualisation is increasingly used in education, limited empirical attention has been given to whether parents and students, who share the same performance information but hold distinct roles, respond to visualised reports through similar behaviours. To address this gap, an interactive radar visualisation was developed to present secondary school students’ achievement across subjects with peer reference points. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework, this study examines the determinants of continued intention to use the visualisation tool. Questionnaire data were collected from 706 parents and 264 students in a Macao secondary school. Structural equation modelling (SEM) revealed fundamentally different ideas of continued engagement. For parents, continued intention was significantly associated with performance expectancy (PE) and effort expectancy (EE), social influence (SI) and facilitating conditions (FC), suggesting the tool functioned as a decision support system for academic planning. For students, only social influence (SI) and facilitating conditions (FC) emerged as significant predictors, indicating that peer comparison and external expectations may not fit their needs. Parents also reported significantly higher continued intention than students. The finding extended UTAUT by demonstrating that core acceptance relationships are moderated by different roles, reframing technology acceptance in educational visualisation from system adoption to information interpretation. The study provides empirical evidence that visualised performance reporting functions not merely as a data display but also as a communication medium whose meaning is actively constructed by users. These insights highlight the need for role-sensitive design, emphasising actionable planning support for parents and personally meaningful, agency-oriented feedback for students, in order to foster productive home–school communication and sustained engagement with learning information. Full article