Search Results (4,918)

The low participation of men in primary education is a persistent and structural phenomenon that cannot be adequately understood through homogeneous views of teachers’ motivations and experiences. This study is conducted in the Chilean context, which is characterized by a highly feminized teaching workforce and persistent challenges related to working conditions, social valuation of teaching, and teacher retention. It aims to analyze profiles of male primary school teachers, considering their motivations, perceptions, and the meanings they attribute to the teaching profession. A sequential explanatory mixed-methods design (QUAN → qual) was employed. First, 144 male in-service primary teachers completed the FIT-Choice scale and a latent class analysis was conducted. Subsequently, in-depth interviews were carried out with an intentionally selected subsample of 20 teachers, which were analyzed using qualitative content analysis. Three distinct motivational profiles were identified: altruistic, pragmatic, and critical. The qualitative findings complemented these profiles, highlighting the influence of personal trajectories and working conditions on teachers’ career choice and retention in the profession. Overall, the findings suggest that policies for training, support, and professional induction must recognize teacher heterogeneity and promote inclusive working environments, moving beyond approaches that focus exclusively on increasing the number of men in primary education. Implications for the design of policies aimed at attracting and retaining male primary school teachers are discussed. Full article

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems. Full article

Ubiquitination is a reversible post-translational modification crucial for cellular homeostasis and protein degradation. It is orchestrated by a cascade of ubiquitin-activating enzymes (E1), conjugating enzymes (E2), and ligases (E3) that tag proteins with ubiquitin, and deubiquitinating enzymes (DUBs) that remove these tags. Through this tightly regulated ubiquitination/deubiquitination system, cells control protein turnover, localization, and activity, thereby governing processes ranging from cell cycle progression and DNA repair to immune and stress responses. Here, we review the structural and functional mechanisms of each class of enzymes in the ubiquitin–proteasome system, including E1, E2, E3, and DUBs, and highlight their roles in key signaling pathways and physiological processes. We further discuss how the dysregulation of these enzymes leads to diseases such as cancer, neurodegenerative disorders, and immune diseases, underlining the potential of targeting ubiquitination pathways for therapeutic intervention. Full article

The origin of life is commonly discussed within two competing conceptual frameworks: the metabolism-first and information-first hypotheses. While each emphasizes a different defining property of early life, modern biochemistry reveals a fundamental interdependence between metabolic processes and genetic information transfer, leading to a persistent chicken-and-egg problem. In this study, we investigate a prebiotically plausible reaction system that enables the concurrent formation of molecular precursors associated with both frameworks. Under simulated Hadean hydrothermal conditions, acetylene, ammonia, cyanide, and carbon monoxide were reacted in aqueous solution in the presence of transition metal sulfides. Using gas chromatography–mass spectrometry combined with stable isotope labeling, we demonstrate the simultaneous formation of the nucleobase uracil and the amino acids alanine and aspartic acid. Isotopic incorporation patterns allow reconstruction of the underlying reaction pathways and confirm the contribution of all starting materials to product formation. While amino acids are produced continuously over the observed period in significantly higher yields than uracil, uracil formation exhibits a pronounced time-dependent maximum after three days. Variations in pH, reaction time, and metal sulfide catalysts modulate product yields but do not prevent the parallel emergence of both molecular classes. These findings support a scenario in which proto-metabolic chemistry and molecular precursors of genetic information could have arisen simultaneously within a shared geochemical setting. The results provide experimental support for a coupled origin of metabolism and transcriptional building blocks, offering a potential resolution to the dichotomy between metabolism-first and information-first models of early life. Full article

Despite their vital physiological roles, oxidative imbalance caused by reactive oxygen, nitrogen, sulphur, and chlorine species damages essential body macromolecules such as proteins, lipids, and nucleic acids through oxidative stress. This stress is strongly associated with cancer, inflammation, neurological and cardiovascular disorders, and other chronic human diseases. Therefore, antioxidants, natural or synthetic, that counteract oxidative damage are important, with increasing interest in their use within the pharmaceutical, food, and cosmetic industries. However, due to toxicity concerns with the synthetic variants, natural antioxidants are increasingly preferred. Extremophile-derived antioxidants, such as superoxide dismutases, catalases, peroxidases, carotenoids, and melanin, are of renewed interest due to their remarkable stability, robustness, and potency under extreme conditions of temperature, pH, and salinity. These make them better than many mesophile-derived antioxidants and excellent candidates for cost-effective biotechnological, research, and industrial processes that require high operational efficiency. This review summarises key classes of selected enzymatic and pigment antioxidants, their mechanisms of action, and their industrial relevance, with a focus on extremophilic microalgae, bacteria, and fungi. The benefits of extremophilic antioxidants are discussed alongside their current applications and existing challenges, including the need to develop efficient delivery systems, scalability issues, and limited characterisation. Full article

Bidentate ligands, derived from the 1,3-dicarbonyl framework, play a central role in coordination chemistry, catalysis, and materials science due to their tuneable donor properties and structural versatility. This review examines and compares three closely related ligand classes, β-diketones (O,O′ donors), imino-β-diketones or enaminones (N,O donors), and di-imino-β-diketones or β-diketiminates (N,N′ donors), to elucidate how systematic substitution of oxygen by nitrogen affects structure and properties. The discussion integrates spectroscopic data (NMR and IR), crystallographic findings, electrochemical measurements, and density functional theory (DFT) calculations reported in the literature. Across these systems, tautomerism plays a decisive role, with conjugation-stabilized enol or enamine forms generally preferred in solution and the solid state. Frontier molecular orbital analyses show extensive delocalization over the chelate backbone and, when present, aromatic substituents. Electrochemical studies reveal consistent correlations between experimental reduction potentials and calculated LUMO energies for O,O′-, N,O-, and N,N′-bidentate ligands. Overall, the comparison demonstrates that donor atom substitution within a conserved conjugated scaffold provides a systematic approach to tuning acidity, coordination behaviour, and redox properties, offering a coherent framework for understanding structure–property relationships in 1,3-dicarbonyl-derived chelating ligands. Full article

Antibiotic residues in aquatic products pose a serious food safety concern, whereas conventional laboratory methods often fail to meet the demand for on-site rapid screening. This study systematically reviews the research progress from 2021 to 2025 on both the risks of antibiotic residues in aquatic products and the development of rapid on-site detection technologies. First, based on a literature survey covering major aquatic products (e.g., fish, shrimp, and shellfish), the widespread occurrence of multiple antibiotics at high concentrations was documented, with quinolones and sulfonamides identified as the most frequently detected classes. To address the need for on-site testing, this review focuses on six rapid detection techniques: fluorescent sensor (FRS), lateral flow immunoassay (LFIA), surface-enhanced Raman scattering (SERS), enzyme-linked immunosorbent assay (ELISA), electrochemical sensor (ECRS), and colorimetric sensor (CRS). The core principles, technical advantages, recent application cases (e.g., integration with smartphones and novel nanomaterials), and development trends for each method are analyzed. Finally, it discusses the current challenges faced by existing on-site detection approaches and their potential solutions. Technology selection strategies tailored to different application scenarios (e.g., aquaculture farms, distribution channels, and consumer-level use) are also proposed. Full article

Background/Objectives: Modern diabetes therapy extends beyond glycemic control and increasingly focuses on comprehensive risk reduction to prevent long-term complications, improve quality of life, and reduce premature mortality. Accordingly, modern therapeutic approaches address not only glucose metabolism but also cardiovascular, renal, and metabolic consequences of diabetes. Within this context, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as a significant therapeutic class. In addition to their well-known effects on glycemic control and the metabolic-cardiovascular-renal axis, increasing evidence suggests that these agents may exert a range of pleiotropic effects and opening new therapeutic venues, discussed in this review. Methods: A narrative review of the literature was conducted using the PubMed, Scopus, and Google Scholar databases. Publications from 2014 and 2026 were screened using predefined keywords related to GLP-1 RAs and their potential effects across multiple physiological systems and diseases. Notably, more than 80% of the included studies were published between 2020 and 2026, reflecting the recent growth of research in this field. Results: GLP-1 RAs have been associated with beneficial effects across a wide range of conditions, including substance use disorders, mental health disorders, neurodegenerative diseases, obesity-related complications, liver disease, genitourinary disorders, osteoarthritis, and sleep apnea. While they are currently the most effective pharmacological agents for the treatment of obesity, they also significantly reduce hepatic steatosis and are associated with a decreased risk of developing hepatocellular carcinoma. Furthermore, they have also demonstrated positive effects against prostate cancer, polycystic ovary syndrome (PCOS), improved libido and fertility. Conclusions: GLP-1 RAs should no longer be regarded solely as antihyperglycemic agents. Instead, they represent a versatile therapeutic class with expanding clinical relevance across multiple medical disciplines. While current evidence is promising, further large-scale, well-designed clinical trials are required to define their full therapeutic potential. Full article

This paper synthesizes classical results in Hodge theory, curvature positivity, and vanishing theorems to give a concise curvature–cohomology criterion for the projectivity of compact Kähler manifolds. While each analytic component—Yau’s solution of the Calabi conjecture, the Bochner–Kodaira–Nakano identity, and Kodaira’s embedding theorem—is well-known, their combination yields a transparent geometric criterion: if the first Chern class $c_1\left(M\right)$ admits a semi-positive real $(1,1)$ representative that is strictly positive at some point (or equivalently has a maximal rank n somewhere), then M is projective. Beyond the maximal rank case, we refine Girbau’s classical vanishing theorem to obtain an optimal rank-sensitive bound: if $2\pi c_1\left(M\right)$ has a semi-positive representative whose pointwise rank is k somewhere, then $H^{p,0}\left(M\right)=0$ for all $p>n-k$. This sharpens the classical Girbau–Griffiths–Harris vanishing theorem and quantifies how partial positivity of a Ricci representative constrains Hodge cohomology. We situate these criteria alongside classical tests (Kodaira integrality and Moishezon) and numerical descriptions of the Kähler cone (Demailly–Paun), discuss deformation-invariance properties, and relate them to RC positivity and Campana–Peternell-type statements. Examples illustrate the sharpness of the hypotheses, and we survey the effective bounds—ranging from rigorous uniform high ampleness results to conjectural optimal constants—with clear distinction between proven theorems, refinements of classical results, and open problems. The contribution of this work lies not in new analytic techniques but in (1) isolating a sharp curvature condition at the level of $c_1\left(M\right)$; (2) organizing classical tools into a direct projectivity criterion; and (3) clarifying the rank-dependent vanishing behavior that follows from partial positivity. Full article

Axions and axion-like particles are compelling candidates for ultralight bosonic dark matter, forming coherent oscillating fields that can be probed by experiments known as haloscopes. A broad range of haloscope concepts has been developed, including resonant cavity haloscopes, lumped-element circuit detectors, and spin-based experiments, each sensitive to different axion couplings and mass ranges. Rather than attempting an exhaustive survey of all existing approaches, this comparative review provides a unified framework for the major haloscope classes, establishing a common language for the descriptions of signal generation, noise properties, analytical methodologies, and scanning strategies. Key properties of ultralight bosonic dark matter relevant for detection are summarized first, including coherence time, spectral linewidth, and stochasticity under the standard halo model. The discussion then compares cavity, Earth-scale, lumped-element, and spin haloscopes, focusing on expected signal shapes, dominant noise sources, and statistical frameworks for axion searches. Particular emphasis is placed on consistent definitions of signal-to-noise ratio and on how detector bandwidth, axion coherence, and noise characteristics determine optimal scan strategies. By systematically comparing operating principles and performance metrics across these detector families, this framework clarifies shared concepts as well as the essential differences that govern sensitivity in different mass and coupling regimes. The resulting perspective synthesizes current search methodologies and offers guidance for optimizing future haloscope experiments. Full article

Polymers are widely used in applications ranging from flexible electronics and thermal interface materials to structural composites and textile fabrics. Their inherently low $\kappa$, strongly governed by molecular structure and morphology, makes polymers a challenging yet scientifically rich class of materials for thermal transport studies. Over the past two decades, modeling and simulation have played a central role in elucidating heat transport mechanisms in polymers and in guiding the rational design of polymer systems with enhanced or tunable thermal properties. This review provides a comprehensive overview of the theoretical frameworks and computational approaches used to model thermal transport in polymers. We discuss atomistic methods including density functional theory, molecular dynamics, and first-principles Boltzmann transport equation approaches, as well as emerging data-driven and machine learning-based techniques. Special attention is devoted to the effects of chain conformation, crystallinity, orientation, interchain coupling, interfaces, and nanocomposite architectures. Current challenges and future research directions are highlighted, with particular emphasis on multiscale modeling, method integration, and predictive materials design. Full article

Intratumoral heterogeneity is a defining feature of cancer, yet standard sequencing and reporting practices often overlook somatic variants present at low variant allele frequencies (VAFs), commonly below 5%. Increasing evidence indicates that these rare alleles can represent clinically meaningful subclones involved in tumor evolution, therapeutic resistance, minimal residual disease, and metastatic dissemination. However, detecting and interpreting low-VAF variants is technically and analytically challenging because background error rates, library artifacts, genomic context, and caller assumptions increasingly overlap with true signal as allele fraction decreases. In this review, we integrate biological and clinical evidence supporting the relevance of low-VAFs and evaluate constraints across sequencing strategies, including whole genome and whole exome approaches and deep targeted panels. We discuss why detectability depends strongly on variant class and genome architecture, with SNVs generally more tractable than indels and structural variants. We then summarize practical approaches that improve sensitivity and specificity beyond increasing depth, including proper tissue handling, molecular enrichment, unique molecular identifiers, duplex-consensus methods, advanced error modeling, and orthogonal validation. Finally, we highlight emerging single-cell, spatial, and multiomic technologies that resolve rare variants in a cellular context. Collectively, these advances support incorporating low-VAF detection into precision oncology frameworks. Full article

Ruthenocene (Rc) and its derivatives form a structurally versatile class of metallocenes with unique and multifunctional applicability. This review presents a detailed analysis of Rc chemistry including the structural comparison with ferrocene, its redox behavior, and substituent effects. We also discuss its applications in sensing, energy storage, photochemistry, and biomedicine. Rc exhibits unique conformational and adaptive electronic properties based on one and two-electron oxidation processes. Electrochemical investigations of Rc to date indicate that its redox behavior is strongly dependent on the electrolyte system, exhibiting quasi-Nernstian characteristics, the formation of stabilized dimeric species [Rc₂]²⁺, and interconversion among Ru(II), Ru(III), and Ru(IV) oxidation states. Rc-based systems exhibit superior performance as redox mediators and labels in electrochemical sensing systems in terms of electron-transfer kinetics, signal amplification, and surface immobilization. In the field of energy storage, Rc decreases the charging overpotential and increases the cycle life of Li-O₂ batteries. Rc further acts as a photoinitiator via charge-transfer-to-solvent and efficient photoinduced electron transfer in metalloporphyrin and fullerene dyads. In biomedical research, Rc derivatives as well as bioconjugates possess promising anticancer activities, displaying reactive oxygen species generation, topoisomerase inhibition, thioredoxin reductase inhibition, receptor-mediated uptake, and target peptide conjugation. Given its flexible ligand design, electrolyte driven redox behaviors, and antiproliferative properties, Rc exhibits a very adaptive molecular scaffold for next generation electrochemical technologies as well as metallodrug design. Full article

Antibody–drug conjugates (ADCs) have become an important class of targeted anticancer therapeutics by integrating the tumor selectivity of monoclonal antibodies with the potent cytotoxicity of small-molecule payloads through rational linker design. This review summarizes the structural fundamentals of ADCs, including antibodies, linkers, and payloads, and describes their coordinated mechanism of action. We trace the evolutionary trajectory of ADCs across three generations, highlighting key breakthroughs, limitations, and representative agents for each era. Furthermore, we elaborate on cleavage mechanisms of linkers (cleavable and non-cleavable). We also categorize and discuss cytotoxic payloads, covering traditional microtubule-disrupting agents, DNA-damaging agents, and novel mechanism-based payloads, along with their modification strategies and preclinical/clinical performance. Finally, we discuss representative and clinically influential ADC designs, with emphasis on the relationships among antibody, linker, and payload. Full article