Search Results (8,968)

Continuous monitoring of railway bridges is essential for ensuring safety and operational reliability, considering aging mechanisms, rising traffic, and elevated speeds of railway vehicles. Frequently, traditional vibration-based approaches, including modal identification and data-driven diagnostic strategies, are strongly influenced by environmental and operational variability, requiring labeled damaged datasets or numerical simulations to provide reliable outcomes. However, the acquisition of complete and representative datasets for training neural networks in structural health monitoring remains a challenging task, particularly for large-scale civil structures such as bridges. In these cases, unsupervised learning approaches represent promising solutions. An unsupervised anomaly detection methodology for railway bridge monitoring based on a long short-term memory (LSTM) autoencoder (AE) trained exclusively on bridge accelerations under healthy structural conditions is proposed in the present work. Specifically, the acceleration responses are obtained from simulations made on a calibrated finite element model of the bridge, reproducing realistic train–bridge interaction scenarios. The multi-channel acceleration signals are reconstructed by the proposed LSTM AE to produce the Root Mean Square Error (RMSE) between measured and reconstructed acceleration responses as indicators of potential structural anomalies. A dual-threshold strategy is adopted for damage detection purposes, including a global threshold for identifying anomalies in the overall dynamic response and per-sensor thresholds derived from the healthy-condition RMSE distribution for detecting localized damages. Only healthy-condition data are required for employing the proposed technique, avoiding labeled damaged data for training purposes. The LSTM AE constitutes an effective and computationally efficient tool for anomaly detection and continuous structural health monitoring of railway bridges, as demonstrated by the obtained results, representing a promising alternative to classical modal-based approaches and existing deep learning-based methods. Full article

GDSL esterase/lipase (GELP) proteins constitute an evolutionarily conserved yet functionally diversified hydrolase family in land plants. They participate in cuticle and secondary cell wall biosynthesis, seed lipid remobilization, reproductive development, and hormone-mediated responses to biotic and abiotic stresses. Despite extensive genome-wide and comparative genomic studies that have categorized large GELPs across numerous crops and model species, only a fraction of members have been functionally characterized in plants, and their catalytic mechanisms and regulatory architectures remain poorly understood. Recent population genomics and cross-species orthogroup analyses in 46 angiosperms have uncovered substantial natural variation within GELP coding sequences and regulatory regions, providing a powerful framework to link allelic diversity to evolutionary trajectories and physiological functions. This review synthesizes current knowledge on GELP evolution, biochemical properties, and roles in development and stress adaptation, and critically evaluates how these insights can be translated into biotechnology and molecular breeding strategies. It highlights emerging resources and concepts from orthogroup-based classification and multi-species datasets that enable systematic discovery of GELP alleles affecting agronomic traits. It further outlines research exploiting GELPs in crop improvement, emphasizing the integration of reverse and forward genetics with multi-omics profiling, biochemical and structural characterization, and gene regulatory network reconstruction. Systematic assessment of the phenotypic impacts of single and combinatorial GELP perturbations on yield, quality, and stress resilience is proposed as a key step toward translating basic insights into breeding and engineering strategies. Full article

Obesity represents a multifactorial health condition influenced by complex interactions among behavioral, environmental, and physiological factors, yet the relative predictive importance of lifestyle behaviors versus medical history indicators remains incompletely characterized. This investigation employed a three-phase machine learning approach to systematically compare the predictive power of behavioral lifestyle factors, medical history variables, and their integration for obesity classification. Phase A utilized a dedicated obesity dataset containing demographic, dietary, and lifestyle predictors to perform seven-category obesity classification, achieving 81.65% test accuracy with an optimized Random Forest ensemble and macro-averaged F1-score of 0.82. Phase B addressed binary obesity classification using health indicators from diabetes screening data, where a Gradient Boosting model with optimized decision threshold achieved 67.84% accuracy and AUC of 0.735, demonstrating substantially lower performance than behavioral predictors. Phase C integrated both feature sets into a unified model, where Gradient Boosting achieved 68.31% accuracy and AUC of 0.747, representing marginal improvement over medical history alone. Cross-validated performance comparisons revealed that behavioral lifestyle factors provided superior discriminative power compared to medical history indicators, with dedicated lifestyle predictors achieving 13.81 percentage points higher accuracy than medical indicators. Feature importance analysis confirmed that transportation mode, physical activity patterns, and dietary behaviors ranked among the most influential predictors in the combined model. These findings demonstrate that behavioral lifestyle factors constitute stronger obesity predictors than medical history variables, with implications for clinical screening strategies and public health intervention targeting that prioritize lifestyle assessment and modification programs. Full article

During the production, storage, and use of solid rocket motors, the impact generated by unexpected accidents, such as collision or drop, will cause damage to the propellant and affect the safety of the motor. However, the progressive evolution mechanism of mesoscopic damage in NEPE propellant under such impact conditions has not been fully elucidated, and there is still a lack of quantitative method to evaluate the impact-induced damage degree, which restricts the engineering safety assessment of solid rocket motors. To investigate the influence mechanism, the mesoscale damage characteristics of NEPE propellant under drop-weight impact is systematically studied. First, damaged NEPE specimens are obtained by conducting drop-weight experiments with a 10 kg hammer, where the drop height is varied to apply different impact impulses. The internal meso-structure of the propellant is then characterized using micro-CT, yielding detailed imagery of the refined meso-structural features and damage morphologies in the NEPE propellant. To capture the dynamic evolution process of mesoscale damage, a mesoscopic model incorporating AP, Al, HMX particles and voids, is subsequently constructed based on the high-precision mesoscopic morphology characterized by micro-CT. By integrating the deviatoric constitutive model, Gurson plastic damage model, and bilinear cohesive zone model, high-fidelity numerical simulations of the drop-weight impact damage process are performed using the advanced SPH-FEM coupling algorithm. The results indicate that no significant damage occurs when the impact impulse is less than 13.85 N·s. As the impulse increases, phenomena including matrix microcracks, void collapse, particle/matrix interface debonding, and main crack formation appear sequentially. When the impulse exceeds 24.25 N·s, particle fragmentation and transgranular fracture occur, accompanied by plastic flow and frictional heating that induce ignition. Finally, the overall damage degree is fitted by the Boltzmann function, and a function for quantitatively describing the damage degree is obtained, which can provide theoretical support for the impact safety assessment of solid rocket motors. Full article

DNA methylation constitutes a primary layer of epigenetic regulation in plants, operating across three sequence contexts (CG, CHG, and CHH) through distinct enzymatic pathways. Over the past fifteen years, accumulating evidence has shown that DNA methylation varies substantially among individuals and populations of wild plants, sometimes independently of underlying genetic polymorphism. This variation can influence gene expression, transposable element activity, and phenotypic traits relevant to ecological adaptation. Population epigenetics, the study of methylation variation at the population scale, has matured from initial surveys using methylation-sensitive amplified fragment length polymorphism (MS-AFLP) into a discipline increasingly reliant on reduced-representation bisulfite sequencing (epiGBS, bsRADseq), whole-genome bisulfite sequencing (WGBS), enzymatic methyl-seq (EM-seq), and direct long-read detection by nanopore sequencing. These methodological advances are opening population epigenetics to non-model organisms across the full breadth of the plant phylogeny, from angiosperms and gymnosperms to ferns and bryophytes. We cover (i) the molecular machinery underlying plant DNA methylation, including the debated status of N6-methyladenine (6mA); (ii) empirical evidence for natural epigenetic variation in plant populations, spanning clonal, invasive, and outcrossing species; (iii) the methodological toolkit available for population-scale methylation profiling, with emphasis on approaches suitable for non-model taxa; and (iv) the ecological and evolutionary significance of population epigenetic variation, including transgenerational inheritance, stress memory, epigenetic clocks, conservation applications, and the emerging integration of epigenetics into the extended evolutionary synthesis. We identify critical knowledge gaps, particularly the near-complete absence of population-level epigenetic data for bryophytes, ferns, and lycophytes, and outline priorities for future research. Full article

This paper investigates whether structured collaboration between multiple large language models (LLMs), each assigned a distinct cognitive role grounded in psychological theory, produces benefits beyond simple answer aggregation. We propose the Parallel Synthesis architecture, in which three cognitively specialized roles Analyzer (hierarchical decomposition), Creative (divergent thinking), and Critic (critical evaluation) process each task independently and in parallel, and a Synthesizer integrates their outputs into a final response. To evaluate collaborative reasoning, we introduce the Emergent Reasoning Score (ERS), a composite metric that separates perspective integration (Synthesis Effectiveness) from novel concept generation (Emergent Value). Experiments on Experiments on the AI2 Reasoning Challenge (ARC-Challenge) (1172 questions) and and the Massive Multitask Language Understanding benchmark (MMLU) (1531 questions) show two consistent findings. First, the architecture achieves high Synthesis Effectiveness ($SE=0.711$–$0.744$), indicating reliable integration of all three cognitive perspectives. Second, Emergent Value remains low ($EV=0.096$–$0.112$), indicating that synthesis primarily recombines existing concepts rather than generating substantial novel content. A Majority Voting baseline achieves comparable or slightly higher answer accuracy than the Synthesizer on both benchmarks, showing that the architecture’s main contribution lies not in answer selection but in producing integrated reasoning traces that draw on multiple perspectives. These findings suggest that the practical value of cognitively grounded multi-agent architectures lies in reliable perspective integration, while ERS provides a reusable framework for distinguishing integration from genuinely novel reasoning in multi-agent LLM systems. The empirical results reported here constitute a pilot validation of the proposed framework on closed-form benchmarks, intended to establish a proof of concept and motivate larger-scale evaluation. Full article

Disability is frequently theorized through a polarized medical-versus-social binary that can obscure the developmental, relational, and sociocultural processes through which bodily difference becomes psychologically meaningful. This study examines how adults with congenital or early-onset physical disabilities narrate and negotiate disability in everyday life, using psychoanalytic concepts as a complementary heuristic lens within an explicitly interdisciplinary framework that integrates developmental resilience and disability theory. Thirty-five in-depth life-story interviews were conducted with seven adults (25–40 years) across approximately five sessions per participant over two months. Data was analyzed using thematic qualitative content analysis, combining systematic coding of manifest content with interpretive attention to symbolic and relational meanings, while cross-checking psychoanalytic interpretations against developmental and social-disability perspectives. Four recurring compensatory patterns were identified: (1) symbolic resignification and verbal normalization (discursive reframing and minimizing disability); (2) achievement-oriented self-positioning (performance and perfectionistic striving); (3) compensatory role assumption (caregiving/protector roles and mastery enactments); and (4) silent family dynamics (familial denial and narrative). Within the specific context of this study, these patterns appeared to function as regulatory efforts to sustain self-cohesion, agency, and belonging. However, the narratives suggest that when these strategies manifest as rigid ideals of ‘overcoming’ and hyper-competence, they may carry a significant subjective cost for participants. Compensatory behaviors are best understood as ecologically embedded regulatory processes shaped by relational resources (experienced as containing/“holding”) and by sociocultural devaluation linked to ableist norms. An integrated model is proposed in which body, self, and environment co-constitute disability across development, clarifying when compensatory strategies support creative adaptation versus defensive rigidity. Full article

This study evaluates the seismic performance and life-cycle economic implications of designing essential urban mid-rise reinforced concrete moment-resistant frame (MRF) buildings to maintain linear elastic behavior up to the Immediate Occupancy (IO) performance level. While most urban buildings are commonly designed to respond non-linearly in order to reduce initial construction costs, the current Mexico City Building Code (MCBC) permits that essential facilities, such as hospitals and schools, maintain linear behavior during moderate-to-strong earthquakes. This code establishes a maximum story drift ratio equal to 0.0075 for essential buildings constituted by MRF subjected to seismic events with a 250-year recurrence interval; in addition, it recommends ductile structural behavior to achieve Life Safety performance at a 450-year recurrence interval. Given the significant differences in occupancy, functionality, and contents of critical facilities, here it is analyzed whether the linear elastic design criterion is efficient for both secondary care hospitals and public schools. Two three-story and five-story MRF buildings, located on firm and transition soil, respectively, are analyzed. This study addresses the probability of brittle-type failure risk, the optimal allowable story drift at the IO performance level, the potential need for use-dependent drift limits, and the contribution of contents and nonstructural components to the total expected seismic losses. The seismic risk and economic performance are quantified through seismic hazard analysis, incremental dynamic analysis, fragility modeling, Monte Carlo simulation, and life-cycle cost evaluation. Full article

This study investigates the influence of adhesive type on the bond performance between CFRP plates and steel interfaces through static tensile double-shear tests. Three types of adhesives (Araldite 420A/B, 2015-1, Sikadur-30CN) were tested under four bond lengths. The results indicate that adhesive strength significantly affects failure characteristics, with distinct material performance differences observed. Bond length influences the stress distribution, enhancing dispersion while potentially altering damage progression. High-performance adhesives exhibit superior shear resistance and fracture energy due to improved viscous properties, whereas moderately plastic adhesives achieve adaptive deformation and durable bonding by enhancing the flow and substrate contact. These findings provide a theoretical basis for material selection in CFRP-strengthened steel structures and offer actionable guidance for structural repair engineering applications. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

Cardiac fibrosis is a central determinant of heart failure progression and arises from pathological remodeling characterized by fibroblast activation, myofibroblast differentiation, and excessive extracellular matrix deposition. In contrast, physiological remodeling permits adaptive cardiac growth without net fibrosis. Pregnancy represents an underexplored physiological model of reversible cardiac remodeling. In response to hemodynamic load, the maternal heart undergoes hypertrophic growth that resolves postpartum, constituting a natural paradigm of fibrosis-resistant cardiac adaptation. Pregnancy and lactation are accompanied by profound endocrine and immune reprogramming of maternal tissues. We propose that this hormonal milieu orchestrates coordinated crosstalk among endothelial cells, fibroblasts, and immune cell populations to suppress profibrotic pathways and preserve extracellular matrix homeostasis. Candidate regulators include estrogen, progesterone, prolactin family peptides, relaxin, oxytocin, and components of the renin–angiotensin–aldosterone system. During the postpartum and lactational period, prolactin and oxytocin may further promote reverse remodeling. These hormones likely act by modulating local cytokine and growth factor networks that otherwise drive fibroblast activation. By focusing on non-myocyte cardiac cells and extracellular matrix dynamics, this review positions pregnancy as a translational model to uncover endogenous anti-fibrotic mechanisms and identify novel therapeutic strategies for cardiac fibrosis. Full article

Heart inflammation and oxidative stress are pivotal pathological drivers in the pathophysiology of various cardiovascular diseases. The present study aims to investigate the beneficial effects induced by extracts derived from edible plants, such as Olea europaea, and sugar cane on heart health. In particular, we investigated the effects of a novel combination constituting Olea europaea, Scutellaria baicalensis, and policosanol extracts on heart, in in vitro and ex vivo models. Olea europaea, S. baicalensis, policosanol extracts and their combination prevented H₂O₂-induced reduction in H9c2 cell (immortalized myoblasts, isolated from rat heart tissue) viability. Moreover, pre-incubation with the combination significantly reduced H₂O₂-induced ROS levels in the same cells. Our present findings also showed that Olea europaea, S. baicalensis and policosanol extracts, as well as their combination, increased lipopolysaccharide (LPS)-induced catalase gene expression at all concentrations tested, in mouse heart specimens. In addition, we also observed that Olea europaea, S. baicalensis and policosanol extracts, as well as their combination, significantly inhibited LPS-induced inducible nitric oxide synthase, cyclooxygenase-2, nuclear factor-kB, and tumor necrosis factor-α gene expression, in the same experimental model. Interestingly, the combination was more effective at decreasing the mRNA levels of all pro-inflammatory markers investigated. Finally, the combination was also able to suppress LPS-induced B-type natriuretic peptide and cardiac troponin I gene expression ex vivo. In conclusion, these findings suggest that this plant-based combination could offer potential benefits for cardiovascular health and support overall heart function in humans. Full article

Clear cell renal cell carcinoma (ccRCC) is characterized by marked biological heterogeneity, which limits the prognostic accuracy of conventional clinicopathological models. Increasing attention has therefore focused on identification of biomarkers that can enhance risk stratification throughout all stages of the disease. Starting from the current state of the art, this narrative review summarizes and critically appraises the evidence published over the past decade regarding prognostic biomarkers in ccRCC. The analysis is structured into four overarching domains: (i) genomic biomarkers, covering somatic alterations and transcriptomic signatures; (ii) tissue-based biomarkers, including immunohistochemical surrogates and immune microenvironment features; (iii) circulating biomarkers, such as systemic inflammation parameters and indices; and (iv) integrated predictive models, represented by emerging multi-omic approaches. Going through the broad framework of potential prognostic biomarkers, emphasis is placed on their individual and integrative value in relation to classic clinical-pathological factors and survival parameters. At the tissue level, chromosome 3p-related alterations constitute a central molecular feature of ccRCC. Among these, BAP1 loss has emerged as one of the most consistently validated indicators of aggressive tumor behavior. Disruption of the SETD2/H3K36me3 axis and immune-related biomarkers, including PD-L1 expression, have demonstrated prognostic associations in selected settings, although with variable and context-dependent performance. In the circulating compartment, plasma KIM-1 has shown prognostic relevance following nephrectomy, while postoperative detection of circulating tumor DNA (ctDNA) may identify patients at increased risk of recurrence. However, limited analytical sensitivity and methodological heterogeneity currently restrict the broader clinical applicability of ctDNA-based strategies. Systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio, show reproducible associations with outcomes but largely reflect host inflammatory status rather than tumor-specific biology. However, no single biomarker currently supports routine prognostic implementation in ccRCC. Future progress will likely depend on integrative models combining genomic, tissue-based, immune, and circulating parameters with established clinical variables. Prospective validation and clear demonstration of incremental clinical utility will be essential before such strategies can meaningfully inform therapeutic decision-making. Full article

Despite broad agreement on sensorimotor foundations of cognition, existing integrative models are not user-friendly to those who most need them—parents, caregivers, teachers, and clinical practitioners. This review addresses that gap by proposing Meaning-Events (M-Es) as sensorimotor–cognitive coordination units that structure meaning from infancy through adulthood. Drawing on joint attention research, embodied cognition, and dynamic skill theory, this integrative model demonstrates how sensorimotor processes—gaze coordination, affective timing/synchrony, bodily orientation, eye–hand coordination, and goal-directed action—organize cognitive structures of increasingly abstract meaning-making. Meaning-Events are shown as the smallest analyzable units that integrate sensorimotor experience with cognition, providing (1) developmental continuity for embodied action giving rise to coherent thought, purpose, and identity; (2) reciprocal perspectives informing impacts of dyadic behavioral interactions; and (3) an analytical and synthetic tool providing visible, measurable differentiation and integration of behaviors over time. Integration of Fischer’s dynamic skill theory with Erikson’s psychosocial theory illustrates applications in clinical and educational contexts. Rather than viewing sensorimotor experience as an early precursor superseded by symbolic cognition, the Meaning-Event model positions these sensorimotor–cognitive coordination units as constitutive of meaning at all developmental levels. Full article

This article examines the concept of the brain (mastiṣka) within the Indian intellectual tradition, tracing its development from the magico-religious medicine of the Atharvaveda (c. 1200–900 BCE) through the classical Āyurvedic texts—the Suśrutasaṃhitā, the Caraksaṃhitā, the Aṣṭāṅgahṛdayasaṃhitā, and the relatively neglected Bhelasaṃhitā—to the subtle-body physiology of Haṭha Yoga literature. Against the background of a comparative analysis with the brain–heart debate in ancient Greek medicine, the article argues that Indian medicine developed a distinctive ‘relational organ model’ in which brain and heart constitute complementary poles of a single vital-cognitive network mediated by the nāḍī (neural-energetic channel) system. This model is neither simply cardiocentric nor encephalocentrist but integrates both within a hierarchical framework. The Bhelasaṃhitā’s unique near-encephalocentrist statement (śiras tālvantare cetanādhiṣṭhānam) reveals a genuine internal debate within classical Indian medicine, while the Haṭhayogic synthesis—locating the ultimate seat of consciousness in the cranial Sahasrāra while preserving the heart as the integrative hub of all channels—represents a coherent integration of both tendencies. The Sāṃkhya philosophical framework provides the metaphysical key to this integration, distinguishing non-material consciousness (puruṣa) from the material cognitive apparatus (antaḥkaraṇa). The article brings into dialogue these historical findings with recent research in neurocardiology, neuroimaging, and prāṇāyāma science to illuminate areas of empirical convergence, contributing to the interdisciplinary dialogue among science, religion, and health on the nature of human flourishing. Full article