Search Results (1,017)

This study presents a comparative analysis of classroom engagement effects on the academic achievement of senior secondary school physics students, focusing on the replication of prior research and contrasting the impacts of guided and minimally guided constructivist instructional approaches. Drawing on established frameworks of inquiry-based instruction, particularly Cognitively Guided Instruction (CGIS) and Cubing Instruction (CIS), the research investigates their relative efficacy in enhancing student learning outcomes. The clustered quasi-experimental pretest–posttest design, involving the Cognitively Guided Instructional Strategy (CGIS) and the Cubing Instructional Strategy (CIS), was adopted by the study. The intact classroom groups of schools purposively selected participated in the study. An achievement test was administered before and after instruction, and the Analysis of Covariance (ANCOVA) and t-tests were used to determine the effects of the intervention while controlling for baseline achievement and mathematical ability. The findings show that the treatment had a significant effect on the students’ achievement (p = 0.030). The t-test result demonstrated that students exposed to the CGIS recorded higher posttest mean scores than those in the CIS group. These outcomes suggests that guided inquiry may offer pedagogical advantages in supporting classroom and conceptual learning. However, the evidence should be cautiously interpreted. The study contributes to the literature as a conceptual replication by providing evidence regarding the effects of guided and minimally guided constructivist approaches in a different instructional setting. The outcomes underscore the importance of balancing instructional guidance and learner autonomy in physics classrooms, as well as the need for further research involving larger samples and diverse contexts to strengthen causal inference. Full article

Background: Mechanistic evidence increasingly implicates fructose exposures as contributors to the development and exacerbation of asthma and other atopic diseases. Proposed mechanisms include gut dysbiosis, impaired epithelial barrier integrity in the gut and airways, metabolic endotoxemia, and amplification of type 2 immune responses. However, epidemiologic findings linking fructose intake with asthma and atopic disorders remain heterogeneous. Objective: To conduct a review of epidemiologic studies evaluating associations between dietary fructose-containing exposures and atopic outcomes in pediatric populations. Methods: A systematic search of PubMed and Embase identified cohort, case-control, cross-sectional, and randomized feeding studies assessing fructose exposure in relation to asthma and atopic outcomes in pediatric populations. Eligibility screening, data extraction, and risk-of-bias assessment were conducted by one reviewer and confirmed by the other. Results: Seventeen epidemiologic studies met criteria. Multiple cohorts (e.g., BRISA, PIAMA) reported modest to moderate associations between higher sugar-sweetened beverage (SSB) intake and pediatric asthma or “asthma traits.” Cross-sectional analyses from NHANES and the National Children’s Study showed stronger associations, with greater fructose exposures linked to two- to five-fold higher odds of asthma. High fructose beverage consumption demonstrated the most consistent positive associations. Large ISAAC-based studies reported largely null findings, reflecting broad dietary exposure categories and limited specificity for fructose-rich beverages. Evidence for rhinitis, eczema, and sensitization was directionally consistent. Conclusions: Despite heterogeneity, the convergence of mechanistic plausibility with epidemiologic signals supports a potential contributory role of high fructose exposure in pediatric atopic disease. More rigorous longitudinal studies with biomarker-based exposure assessment are needed to refine causal inference. Full article

Background/Objectives: Sarcopenia (SP) and osteoporosis (OP) are common yet underrecognized complications of liver cirrhosis, contributing to increased morbidity and mortality. Their coexistence, termed osteosarcopenia (OS), represents a compounded musculoskeletal impairment. Insulin-like growth factor 1 (IGF-1), synthesized in the liver, has been implicated in muscle and bone metabolism. This study aimed to assess the prevalence and association of laboratory and clinical parameters with SP, OP, and OS in cirrhotic patients, with a focus on IGF-1 deficiency and their impact on mortality. Methods: This cross-sectional study included 100 cirrhotic patients at a tertiary center. Sarcopenia was diagnosed using CT-derived L3 skeletal muscle index and osteoporosis via the DEXA scan. IGF-1 levels and metabolic parameters were measured. Multivariate logistic regression identified laboratory and clinical factors associated with musculoskeletal complications. However, due to the cross-sectional design, causal relationships could not be inferred. Results: SP, OP, and OS were present in 41%, 22%, and 11% of patients, respectively. IGF-1 levels were significantly lower in patients with SP, OP, and OS (p < 0.05) and were independently associated with increased risk of SP (OR = 1.797, p = 0.006), OP (OR = 1.873, p = 0.045), and OS (OR = 2.326, p = 0.003). Mortality rates were significantly higher among patients with OS (72.7%), OP (77.3%), and SP (56.1%). OS conferred the highest adjusted mortality risk (OR = 2.739, p = 0.009), followed by SP (OR = 2.278, p = 0.015) and OP (OR = 1.958, p = 0.036). Conclusions: Musculoskeletal complications are highly prevalent and predictive of mortality in cirrhosis. IGF-1 deficiency is a strong independent biomarker for SP, OP, and OS. Routine screening and early intervention targeting IGF-1 pathways and nutrition may improve outcomes in this population. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

As a transformative force, the digital economy serves as a critical engine for driving high-quality economic development and fostering New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality. Viewing the establishment of China’s National Digital Economy Innovation and Development Pilot Zones as a quasi-natural experiment in economic system management, this study employs a Double Machine Learning (DML) framework to evaluate its systemic impact on A-share listed companies from 2015 to 2023. Unlike traditional linear models, the DML approach flexibly controls for high-dimensional confounding variables and functional form misspecification, thereby ensuring highly rigorous causal inference. The empirical results demonstrate that these pilot zones create an optimized “digital environment” that significantly enhances corporate NQPF, a conclusion that remains highly robust across a comprehensive battery of robustness and endogeneity tests. Mechanism analysis reveals three systemic transmission pathways through which the policy operates: optimizing factor allocation, deepening digital technology empowerment, and promoting green innovation and sustainability. Furthermore, heterogeneity analyses indicate that the policy’s efficacy varies significantly across corporate profiles, manifesting most prominently in non-state-owned enterprises, high-tech firms, and those located in eastern regions. These findings provide robust micro-level evidence for policymakers aiming to optimize digital economic systems and accelerate the systemic formation of advanced productive forces. Full article

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management. Full article

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML–LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments. Full article

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article

Chronic crop diseases caused by uncultured, obligate, or host-dependent pathogens challenge traditional pathogen-centric paradigms that often interpret symptoms as direct outcomes of pathogen toxins, effectors, or tissue colonization. Here, we advance a host-centric immune framework that reframes disease as an emergent consequence of dysregulated host immune network activity, including prolonged activation, signaling miscoordination, and systemic physiological disruption. Using citrus huanglongbing (HLB) as a primary exemplar and canola clubroot as a parallel system, we synthesize evidence that persistent immune stimulation can drive self-damaging outputs, including sustained reactive oxygen species accumulation, chronic vascular and transport dysfunction, hormone imbalance, and growth–defense trade-offs. While many observations derive from transcriptomic, physiological, and genetic studies conducted under controlled experimental conditions, the available evidence collectively suggests that persistent immune activation may contribute substantially to disease-associated decline in these systems. We argue that pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) operate as an integrated immune network whose feedback structure can become destabilized under chronic infection, generating immune states that are simultaneously harmful and often ineffective at pathogen clearance. We further discuss how panomic profiling, spatially resolved analyses, and network inference can diagnose host immune states at tissue and cell-type resolution, and how genome editing enables causal tests and rational immune tuning strategies that optimize defense amplitude, timing, and localization rather than indiscriminately amplifying resistance. By centering the host immune system as both a source of protection and pathology, this framework provides a conceptual and practical roadmap for understanding and engineering resilience in HLB, clubroot, and other chronic crop diseases in which pathogen biology remains experimentally opaque. Full article

Background/Objectives: Over the last two decades, there has been a substantial change in the understanding of post-traumatic hypopituitarism (PTHP), which is no longer regarded as a marginal phenomenon. Clinical manifestations of pituitary hormone deficiency are frequently nonspecific, with fatigue and cognitive dysfunction predominating. Given that head injuries currently constitute a global burden for healthcare systems, the aim of the present study was to determine whether self-reported post-mild traumatic brain injury (mTBI) symptoms that may indicate hypopituitarism reflect true pituitary insufficiency or are attributable to other hormonal aberrations. The study aimed to assess the relationship between self-reported symptoms of PTHP and hormonal test results following mTBI. Setting: Patients were recruited from a tertiary trauma center Emergency Department (ED) in northern Poland from January 2023 to October 2025. Participants: The participants were adult (18 > y.o.) individuals with mTBI who met the inclusion criteria. Design: This was a prospective cohort study. During their post-head injury admission to the ED, patients had a blood sample taken. The procedure was repeated consecutively after 3, 6 and 12 months. After 6 and 12 months, patients were asked to complete a questionnaire. Methods: Pituitary and thyroid hormones were measured using the chemiluminescence immunoassay method and the heterogenous immunochemiluminescence method. The questionnaire used, Questionnaire for the Assessment of Symptoms of Anterior Pituitary Insufficiency in Patients After Mild Traumatic Brain Injury (mTBI) Hospitalized in the Emergency Department, was designed for the purposes of this study. Results: Self-reported symptoms suggestive of anterior pituitary dysfunction following mTBI were not confirmed by laboratory assessment of pituitary hormones. However, after 6 months, a statistically significant correlation was found between the number of reported symptoms and prolactin levels (ρ = 0.730; p = 0.0013), whereas after 12 months a downward trend in free triiodothyronine (fT3) levels was observed compared with the baseline. Conclusions: Persistent symptoms reported by patients following mTBI at 6 and 12 months, particularly fatigue and impaired concentration, showed statistical associations with prolactin levels at 6 months and lower fT3 levels at 12 months. These findings reflect correlations identified in the statistical analysis and do not support inferences regarding causality or the presence of true PTHP. Full article

An inverse correlation between serum vitamin D levels and hidradenitis suppurativa (HS) severity is frequently reported, yet the causal nature and direction of this association remain unresolved. A systematic review was conducted following PRISMA guidelines, identifying 12 relevant studies. A two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW) method was subsequently performed using genetic instruments for vitamin D from the UK Biobank (n = 417,580) and HS summary statistics from FinnGen (n = 1420). The systematic review confirmed a high prevalence of vitamin D deficiency (<20 ng mL⁻¹) among HS patients (weighted mean 17.90 ng mL⁻¹) and identified inverse correlations between vitamin D levels and disease severity, active lesions, and C-reactive protein (CRP), while supplementation improved clinical outcomes. A null MR estimate consistent with the absence of a detectable average linear causal effect of lifelong genetically predicted 25(OH)D levels on HS risk in the analyzed population was observed. Sensitivity analyses yielded consistent null results with no significant horizontal pleiotropy. The results suggest that hypovitaminosis D is likely a marker of the systemic inflammatory state rather than a direct causative factor. The observed clinical benefits of vitamin D supplementation warrant further interventional studies to define its potential therapeutic role. Full article

Background: Dietary calcium and dairy products are hypothesized protective factors against metabolic syndrome (MetS), yet epidemiological evidence remains inconsistent. This systematic review and meta-analysis evaluated the association between total dietary calcium intake or dairy consumption and MetS prevalence in adults. Methods: Following PRISMA 2020 guidelines, PubMed, Cochrane Library, ClinicalTrials.gov, and SCOPUS were searched through to October 2025 for eligible cross-sectional studies assessing dietary calcium or dairy intake and MetS (NCEP ATP III, IDF, or JIS criteria). Longitudinal studies, non-English articles, and pediatric populations were excluded. Quality was assessed via an adapted Newcastle–Ottawa Scale. Random-effects meta-analyses pooled fully adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing the highest versus lowest intake categories. Results: Twenty-four studies were included (12 for dietary calcium intake, 12 for dairy products). Higher dietary calcium intake was significantly associated with lower MetS odds (pooled OR: 0.85; 95% CI: 0.80–0.91), despite substantial heterogeneity (I2 = 70.1%). Higher dairy consumption was also inversely associated with MetS (pooled OR: 0.78; 95% CI: 0.72–0.85; I2 = 64.6%). While small-study effects were observed for dairy, trim-and-fill analysis confirmed the robustness of the findings. Higher calcium intake further correlated with favorable profiles in individual MetS components, including blood pressure, HDL cholesterol, waist circumference, triglycerides, and fasting glucose. Conclusions: Higher total dietary calcium intake and dairy product consumption are associated with a lower prevalence of MetS in adults. However, the cross-sectional nature of the included studies precludes any inference of causality between calcium intake and MetS. Therefore, although these findings suggest a protective role of calcium-rich diets, well-designed prospective and interventional studies are warranted to clarify whether this relationship is causal. Full article

Accurate prediction of photovoltaic (PV) power is crucial for ensuring grid stability. The belief rule base (BRB) is a rule-based expert system capable of effectively handling nonlinear causal relationships. Therefore, it can be applied to PV power prediction. In practical prediction scenarios, a high-quality initial model can produce more accurate predictions. However, obtaining sufficient expert knowledge to determine the structure and parameters of the BRB is usually difficult. To address this issue, a PV power prediction method is proposed based on a data-driven interval construction belief rule base (DD-IBRB), which reduces the reliance on expert knowledge during model construction. First, a fuzzy clustering algorithm is designed to construct reference intervals. Then, a Gaussian membership interval function (GIBM) strategy is proposed to initialize the belief degrees. Next, a representative point selection mechanism is designed within the reference intervals. Model inference is subsequently performed based on evidential reasoning (ER) rules. Finally, a multi-population evolution animated oat optimization with parameter constraints (MEAOO) is used to optimize the DD-IBRB model. Taking the PV power output as a case study, the mean squared error is 0.00056, indicating that the proposed DD-IBRB method can effectively complete modeling and obtain accurate prediction results. 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