Highly imbalanced intelligent identification of civil aviation maintenance hazards based on text mining and selective ensemble modeling

The long-standing debate regarding the association between family adversity, irritability, and psychopathology centers on whether it manifests in a domain-general or domain-specific manner. This study seeks to advance the discussion by examining the direct and indirect effects of irritability on the associations between family adversity and psychopathology, with particular attention to individual differences in irritability susceptibility. Data was drawn from a longitudinal study (N = 4,313; 50.3% girl; Mage = 13.17, SD = 1.81), in which irritability, psychopathology, and family adversity were assessed by questionnaires. Adolescents manifested in both domain-general and partially domain-specific ways: some adolescents exhibited both externalizing and internalizing psychopathology under the influence of phasic and tonic irritability, while others displayed externalizing psychopathology specifically associated with phasic irritability, or internalizing psychopathology specifically linked to tonic irritability. Furthermore, family adversity was associated with psychopathology both through a common irritability pathway and specific pathways where particular types of adversity affected specific psychopathology via distinct irritability dimensions. This study provides a preliminary map of the domain-general and domain-specific pathways linking family adversity, irritability, and psychopathology, thereby offering a framework to guide targeted interventions aimed at preventing the progression to externalizing or internalizing psychopathology.

Excessive accumulation of fibrillar collagen causes pathological scarring and fibrosis. A promising anti-fibrotic strategy targets the extracellular assembly of collagen fibrils rather than intracellular synthesis pathways. We previously developed a chimeric monoclonal antibody targeting the C-terminal telopeptide of the α2(I) chain of human collagen I that effectively disrupts fibrillogenesis. This study details the engineering of a humanized antibody variant optimized for therapeutic application, augmented with a collagen-binding peptide (CBP) to enhance targeted retention in fibrotic tissues. A humanized ACA was engineered by in silico homology modeling, complementarity-determining region grafting, and sequence optimization to eliminate chemical liabilities. Variants were expressed in mammalian cells and evaluated for binding kinetics and specificity. To improve spatial localization, the CBP was fused to the antibody. The lead variant was assessed for in vitro cytotoxicity, matrix retention, and in vivo efficacy using a rabbit model of post-traumatic knee arthrofibrosis. The humanized ACA variants maintained high specificity and affinity for the α2Ct target domain. Fusing the CBP to the C-terminus of the light chain (C-cbpACA) successfully enhanced matrix retention without compromising target engagement or causing cellular toxicity. In the rabbit arthrofibrosis model, intra-articular C-cbpACA delivery significantly reduced flexion contracture and decreased total collagen deposition in the joint capsule compared to untreated controls. We successfully engineered a clinically viable, humanized, and matrix-targeted anti-fibrotic antibody that specifically inhibited extracellular collagen assembly and exhibited enhanced localization within fibrotic tissues. This construct represents a promising therapeutic strategy for mitigating pathological scarring and improving post-traumatic functional outcomes.

Abstract This paper shows that music industry and EU initiatives at the start of the online era for music consumption between the early 2000s and the early 2010s, aiming for centralized copyright databases, failed in part due to misaligned remuneration systems and economic priorities. Some challenges present since those early years have remained, while new ones have emerged with the advent of music streaming, and more recently also with generative AI (GenAI) music tools and services. Decentralized solutions also still have to grapple with metadata design challenges for attaining music metadata interoperability, generally with regard to domain specificity, granularity and provenance. The transparency obligations in Articles 50 and 53(1)(d) of the AI Act do not provide sufficient practical, enforceable rules that can improve metadata interoperability or copyright attribution for GenAI music in the (European) music industry. The explanations and guidance given in the First Draft Code of Practice in relation to Article 50 or the Explanatory Notice and Template for Article 53 do not sufficiently fill those gaps either.

Background One of the significant determinants of teaching and learning outcomes in students is teacher efficacy. Clinical teaching plays an educational role in nursing as the connecting link between theory and practice. Nevertheless, the effectiveness of clinical nursing education is usually affected by the abilities, readiness, and the skill of nursing educators. Despite the fact that a number of teacher efficacy scales have been designed across various nations, there is a paucity of situation-specific scales to assess teaching efficacy in clinical nursing education in India. Aim To design a Teaching Efficacy Scale (TES) and a module to measure the clinical nursing instructions provided by teachers in a few nursing colleges of Northern India. Methods Both quantitative and methodological cross-sectional design are applied. The research is undertaken in the nursing colleges of Haryana, Punjab and the Delhi-NCR. Stratified proportionate random sample selection is used to choose a total of 500 B.Sc. Nursing students in 2nd, 3rd and 4th years. Delphi technique is sampled by the experts. The TES is created in a threestep, ten-step process that involves specification of content domain, item pool generation, content validity evaluation, questionnaire development, pilot study, dimensionality evaluation, reliability evaluation, and construct validation. Data analysis is done through SPSS where descriptive and inferential statistics are used. Results The Teaching Efficacy Scale is likely to measure various aspects such as pedagogical learning environment, role of teacher and evaluation components. The psychometric characteristics of the scale will be determined by reliability and validity testing. Conclusion The TES is a universal tool for assessing the effectiveness of clinical teaching and pinpointing levels of weakness in clinical nursing education, thus making a contribution to the enhancement of nursing education and the advancement of clinical competency.

Domain specificity and magnitude of cognitive-psychosocial associations in major depressive disorder: A systematic review and meta-analytic approach with multilevel sensitivity analyses.

A targeted sequencing and genome mining approach for delftibactin-like biosynthetic pathways revealed three distinct biosynthetic gene cluster architectures (BGC del, dlc and dlp) encoded in genomes of members of the genus Delftia. Comparative metabolomic analysis guided the isolation and characterization of a yet unreported metallophore, delftichelin A from Delftia deserti DSM1621 (previously named Delftia acidovorans DSM1621). Prediction of BGC architecture and A domain specificity was in line with the structure analysis uncovering previously unreported differences in amino acid composition and modifications. Analysis of bioactivity and metal-binding characteristics demonstrated that delftichelin A shows a preferential affinity for ferric iron, while also exhibiting heavy metal detoxification mechanisms via oxidative degradation, analogous to those reported for the delftibactin family of compounds.

Since 2020, Medicare Advantage (MA)-related internet searches have tripled, accompanied by increased regional marketing by private insurers. Commercial health insurance dominates the internet during enrollment periods, often outpacing public sources in accessibility. Prior studies suggest that MA advertising significantly shapes enrollment and may fuel choices over traditional Medicare in certain subpopulations. We sought to better understand how health plan marketing strategies affect consumers by using Google Trends data and MA health plan enrollment selection. We applied novel analysis to assess statistical relationships among marketing, internet searches, and enrollment data. The objectives of this paper are (1) to establish the validity of Google Trends data as a surrogate measure for consumer MA plan selection by demonstrating stable, repeatable seasonality and domain specificity using control terms such as "car insurance" and "life insurance" at national and Designated Market Area levels; (2) to quantify the congruency between MA search interest and Centers for Medicare & Medicaid Services enrollment data by testing whether search peaks coincide with or precede enrollment surges nationally within a year; and (3) to assess whether local search intensity aligns with advertising exposure by evaluating search behavior as a potential proxy for marketing impact and consumer engagement. This study is a retrospective Google Trends analysis of consumer search patterns from January 2004 to December 2024, using relative search volume and conducting correlations with MA enrollment. Search data are accessible via the Google Trends website Explore tool or by applying for Google Trends application programming interface alpha access. MA enrollment data originated from the Centers for Medicare & Medicaid Services MA Dashboard. KFF (formerly the Kaiser Family Foundation) provided the medical advertising marketing data. A consistent, significant correlation between MA advertising and searches on MA exists across US markets, particularly before and during MA enrollment windows. Findings suggest a linkage in user behavior between volume of searches and subsequent enrollment in an MA plan. Internet search data can provide an open, near-real-time means of tracking patterns in MA-related search activity across time and geography, offering insight into how consumer interest fluctuates around enrollment periods. Our analysis reveals repeatable patterns in consumer interest over time that may be useful for contextualizing insurance marketing dynamics of consumers choosing commercial MA over traditional Medicare benefits. We also identified a significant correlation of seasonal trends in searches using terms associated with MA plans that peaked during the annual enrollment period (October-December). Improved accessibility to Medicare resources and directed messaging can bridge information gaps for underserved populations and can lead to more cost-effective decision-making by Medicare-eligible beneficiaries.

As generative artificial intelligence (AI) is increasingly integrated into employees' daily workflows, it is profoundly reshaping the nature of work, which raises critical theoretical questions about how employees can build sustainable careers. Drawing on approach-avoidance motivation theory, this study distinguishes between two types of proactive employee adaptation to AI (i.e., AI job crafting): an approach-oriented type aimed at leveraging AI to expand job boundaries and enhance personal capabilities, and an avoidance-oriented type involving contractive or defensive strategies to mitigate the negative perceptions of AI. Based on this distinction, this study develops and tests a dual-pathway mediation model. Data were collected through a multi-source, multi-wave survey of 287 employee-leader dyads in China, utilizing the newly developed and validated AI Job Crafting Scale. The findings indicate that AI approach job crafting positively predicts professional proximal indicators of career sustainability (i.e., career satisfaction and performance) by enhancing work meaningfulness, whereas AI avoidance job crafting negatively predicts them via work alienation. Notably, both pathways failed to significantly affect life satisfaction, providing compelling evidence for the domain specificity of AI-related psychological mechanisms. Furthermore, work autonomy not only strengthens the positive impact of AI approach job crafting on work meaningfulness but also weakens the positive effect of AI avoidance job crafting on work alienation. This study contributes a dual-pathway model and measurement tool for AI job crafting, highlighting employee autonomy as a key practical strategy.

This paper presents an effective approach for lower-order (LO) modeling of an electric vehicle–integrated off-grid microgrid (OMG) system. The seventh-order system (SOS) of the OMG is reduced to a second-order model (SOM) while preserving the original system’s dynamic characteristics and ensuring computational efficiency. The Taylor series (TS) and Laurent series (LS) expansions are employed to simplify the complex system that plays a significant role in the reduction process. The expansion parameters of the higher-order system (HOS) of OMG and its lower-order model (LOM) are exploited to construct the fitness function. The proposed approach constructs three sub-objective functions based on TS and LS. These sub-objective functions are then combined into a single fitness function to obtain an improved LOM by enhancing the transient and steady-state responses with respect to the HOS of OMG. To minimize the error, the resultant fitness function is optimized using the brown bear optimization (BBO) algorithm. The optimization is performed under two key constraints: (i) ensuring zero steady-state error, and (ii) satisfying the Hurwitz stability criterion. To demonstrate the efficacy of the proposed LOM, it is compared with other LOMs obtained from different approximation techniques. The proposed LOM and other LOMs are graphically validated through step, impulse, Bode, Nichols, and Nyquist response comparisons with the HOS. Additionally, the performance error criteria (PEC), time-domain specifications (TDSs) and frequency domain specifications (FDSs) of the proposed LOM are compared with other LOMs using the HOS to establish the validation and applicability of the proposed method.

Conventional moderation practices, which rely on human reviewers to identify and remove harmful contents are often labor-intensive, subjective, and unable to cope with the massive volume of user-generated data produced daily. Hate text speech has become a pervasive challenge across digital platforms, prompting extensive research into automated detection methods capable of identifying harmful and abusive content at scale. This review provides a comprehensive synthesis of machine learning approaches for hate speech detection, examining the linguistic characteristics of hateful expressions, the evolution of datasets, and the progression of modelling techniques from traditional machine learning to deep learning and transformer-based architectures. The analysis highlights the complexity of hate speech as a sociolinguistic phenomenon, particularly in its implicit, coded, and context dependent forms, which remain difficult for automated systems to detect reliably. Significant limitations in existing datasets including annotation inconsistency, class imbalance, domain specificity, and limited multilingual coverage further constrain model performance and generalization. Across the literature, challenges related to bias and inadequate evaluation practices persist. By synthesizing current trends and identifying gaps. This review outlines key research directions focused on contextual modelling, multilingual and cross-cultural resources, implicit hate detection, fairness aware algorithms, and adaptive learning strategies. The findings underscore the need for interdisciplinary with ethically grounded approaches to develop robust and socially responsible hate speech detection systems capable of supporting safer online environments. Overall, hate speech detection remains an evolving field that requires ongoing refinement of datasets, models, and evaluation practices. By addressing current gaps and embracing innovative approaches, future systems can better support the creation of safer and more inclusive digital environments.

This article advances a structural framework arguing that some mythological systems offer structural models that can illuminate aspects of contemporary creative processes in contexts marked by uncertainty, complexity, and ambiguity. Rather than proposing mythological belief or practice as a source of creative guidance, the article treats mythology as a cultural archive of cognitive structures that encode recurring patterns in human creativity. Drawing primarily on Hindu and Greek traditions, the analysis demonstrates that mythological configurations—such as the Ganesha–Yama dialectic and the system of the Nine Muses—anticipate and illuminate empirically validated dynamics in modern creativity research, including exploration–exploitation tensions, generative–selective cycles, domain specificity, and cross-domain synthesis. By integrating insights from structural anthropology, sociocultural creativity theory, computational creativity, and innovation studies, the article shows that mythological structures function as meta-procedural frameworks rather than prescriptive methods. These frameworks make visible liminal states, escalating constraints, and meaning-oriented judgment processes that remain undertheorized in contemporary models. The article concludes by arguing that structurally informed dialogue between mythological analysis and creativity research can enrich theoretical understanding without romanticizing or mystifying creative work.

Source-free unsupervised domain adaptation (SFUDA) aims to improve performance in unlabeled target domain data without accessing source domain data. This is crucial in scenarios with data-sharing restrictions due to privacy or compliance constraints. Existing SFUDA approaches often rely on pseudo-labeling techniques based on entropy or confidence metrics. These often overlook fine-grained data features, resulting in noisy pseudo-labels that degrade model performance. To overcome this limitation, we develop a new method called fine-grained pseudo-labeling and feature alignment (FGPLFA) to enhance SFUDA's performance. FGPLFA starts with a gradient-based metric that integrates insights from both model knowledge and data features, creating a more reliable sample metric. To enhance fine granularity, the fine-grained pseudo-labeling (FGPL) module was introduced. This module clusters data based on the magnitude and direction of gradients, allowing for dataset partitioning into subsets at the sample level. The subsets are pseudo-labeled with category-specificity and domain specificity, establishing a multilevel granularity structure that reduces noisy pseudo-labels. Subsequently, the mean-covariance adjustment feature alignment (MCAFA) method was introduced. Features from the subsets are aligned in a specified sequence, enhancing model adaptability in the target domain. Extensive experiments conducted across multiple datasets validate the superiority of FGPLFA.

Cognition is a dynamic process and is subject to substantial variation across short and long timescales. It is becoming common to assess cognition repeatedly over short intervals to determine the correlates and consequences of such "cognitive variability." A high-frequency cognitive assessment approach is also an ideal method for measuring how cognition operates in daily life. Nevertheless, several fundamental questions regarding the nature of cognitive variability remain unanswered. We utilize data from the COGITO study, which administered nine separate cognitive tests to more than 200 participants for 100 days to answer the following questions: Do different tasks exhibit similarly reliable levels of variability, and does variability cluster into distinct cognitive domains? This rich data set was analyzed using Bayesian mixed-effects location scale models which simultaneously estimate individual means and variability. All nine tasks exhibited significant variability across the 100 days of testing. Tasks within the domains of episodic memory or processing speed were moderately correlated with each other suggesting some degree of domain specificity. Working memory tasks, on the other hand, did not correlate well with each other suggesting variability in these tasks is dominated by momentary or task-specific influences. These findings not only advance our theoretical understanding of what cognitive variability is but also provide insight into which cognitive tests are most suitable for high-frequency administration and thus may be most amenable to use for studying aging and cognitive processes as they occur in daily life. Appropriate limits on the generalizability of our results are noted. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Circadian rhythms regulate sleep-wake cycles and modulate cognitive functions over a 24-hour period. Following sleep loss, certain cognitive performance partially rebounds in the early evening, a phenomenon known as circadian rescue. Yet, the magnitude and domain specificity of circadian rescue remain poorly understood. Here, we integrate experimental and meta-analytic approaches to differential contributions of circadian and homeostatic processes to cognitive rescue following sleep deprivation. In Study 1, 54 healthy adults remained awake for 35 consecutive hours while repeatedly completing the Psychomotor Vigilance Task (PVT), the Digit Symbol Substitution Test (DSST), and the Karolinska Sleepiness Scale (KSS). Performance dynamics were modeled using the two-process framework of sleep regulation. In Study 2, a meta-analysis of published data contextualized these findings across protocols. Results reveal domain-specific circadian recovery rates of 33.0%-52.1% for PVT, 45.7% for DSST, and 23.5% for KSS, indicating that subjective sleepiness is predominantly driven by homeostatic load, whereas objective cognitive performance retains significant circadian modulation under conditions of acute homeostatic pressure. These findings clarify how circadian and homeostatic drives interact to shape cognitive task performance and subjective sleepiness outcomes under sleep loss, with practical implications for optimizing performance in fatigue-prone environments.

Individuals perform many tasks at an optimal rate that is consistent within but not between individuals, evidenced by the spontaneous rate at which one performs a task in the absence of external rate cues. We tested three hypotheses concerning how spontaneous production rates (SPRs) are generated and associated across language and music tasks: Biomechanical constraints associated with effector systems (vocalized/fingered: H1), reliance on auditory feedback (presence/absence: H2), and domain-specific constraints (speech/music: H3). We tested these hypotheses by having participants produce music and speech sequences, sequences that used vocalized or fingered effectors, plus a Silent finger-tapping condition to test the influence of auditory feedback on spontaneous production rates. SPRs were significantly correlated across all tasks that involved production of auditory feedback, regardless of effector or domain. However, Silent Tapping rates were not significantly correlated with any SPRs that produced auditory feedback. Together, these findings suggest that the generation of auditory feedback plays a critical role in the spontaneous rate at which participants engage in rhythmic motor actions, more so than the biomechanical constraints of effector systems.

Luciano Floridi’s concept of distant writing – which is the textual production mediated by generative AI – has presented us with a challenge to traditional accounts of authorship and epistemic agency. In this paper, we analyze the epistemological consequences of this new paradigm, arguing that distant writing reframes rather than diminishes the author’s role, shifting epistemic competence from direct composition to second-order curatorial acts: architectural design, dialogical refinement, evaluative verification, and integrative synthesis. Applying Ernest Sosa’s virtue epistemology, we develop the Apt Curation Model, which positions the human author as the central epistemic agent, not as sentence originator, but as virtuous curator whose competence makes the text an apt epistemic performance. We rebut the opacity objection that AI’s inscrutability reduces authorship to epistemic luck, demonstrating through Floridi’s Levels of Abstraction that curatorial virtues constitute responsible engagement. We conclude by examining domain specificity and identifying implications for pedagogy and intellectual property law.

Infections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur during and after treatment. When deciding on the type of CLL treatment, the risk of infections is typically assessed based only on age and comorbidities; therefore, there is a need to develop a predictive model that incorporates information from multiple data sources. However, training an effective machine learning model requires a large sample size. Patient/material and methods: In this study, we developed a machine learning approach using domain adaptation (DA) to predict the risk of severe infection during treatment in patients with CLL. We implemented a DA strategy using lymphoma patient data and compared it with a domain-specific (DS) strategy across multiple models. The DA strategy outperformed the DS strategy across all models, with an odds ratio of 4.43 for infection risk between high-risk and low-risk groups, compared with an odds ratio of 3.69 for the best DS model and 2.27 for the CLL-IPI alone. Explainability analysis identified predictive features for both the DA and DS models, including medication data and biochemistry tests. Specifically, C-reactive protein levels and non-therapeutic drugs were common features identified by both DA and DS models, while the DA models relied more heavily on alimentary tract drugs, solvents and diluting agents, and antibacterial medications. These findings highlight the value of integrating data from different diseases (lymphoma) to improve predictions in a target disease (CLL), and represent a step toward data-driven identification of CLL patients at high risk of infection during treatment.

Pyroptosis is an inflammatory cell death pathway that is a key defense mechanism of intestinal epithelial cells. To successfully establish an infection, intracytosolic gram-negative pathogens must block this host response. Indeed, a Shigella effector OspC3, injected into cells by its type III secretion system, suppresses epithelial pyroptosis by targeting and inactivating caspase-4 (CASP4). Here, we demonstrate that OspC2, which shares 96% identity with OspC3, targets caspase-5 (CASP5), a close paralog of CASP4. Through a combination of yeast two-hybrid, transfection, and bacterial infection assays, we show that the distinct pyroptotic caspase specificities of OspC2 and OspC3 are determined by a short α-helical region, designated the pyroptotic caspase specificity (PCS) domain. This domain is located upstream from the ankyrin-rich repeat (ARR) region previously established to promote OspC3 binding to CASP4. Swapping PCS domains between OspC2 and OspC3 is sufficient to redirect their caspase targeting. Evidence for CASP5-driven pyroptosis in response to infection has not yet been established. However, CASP5 displays signatures of positive selection at residues predicted to interact with the PCS domain of OspC2. Notably, the introduction of orangutan-specific residues into human CASP5 disrupts its interaction with and modification by OspC2, demonstrating that CASP5 natural variation can cause key functional differences in this host-microbe molecular interaction. These findings highlight the evolutionary interplay between bacterial effectors and host proteins, support a likely role for CASP5 in responding to gram-negative bacteria, and identify promising therapeutic targets for enhancing epithelial defense against bacterial pathogens.IMPORTANCEShigella species are human-specific gram-negative pathogens that establish a replicative niche in intestinal epithelial cells by blocking pyroptosis, a key inflammatory cell death pathway. We reveal that two closely related Shigella type III secreted effectors, OspC2 and OspC3, specifically inactivate the human caspase paralogs CASP5 and CASP4, respectively. This specificity is determined by their newly identified pyroptotic caspase specificity (PCS) domains. In addition, we find that positively selected residues in CASP5 alter the OspC2/CASP5 interaction, underscoring the impacts of ongoing evolutionary arms races between bacterial effectors and host immune proteins. By elucidating the molecular basis of caspase targeting and adaptation, this work provides new insight into the diversification of host defense mechanisms and identifies potential therapeutic targets for enhancing epithelial resistance to bacterial infection.

(1) Background: Large language models (LLMs) show promise in healthcare but are constrained by hallucinations, static knowledge, and limited domain specificity. Fine-tuning (FT) and retrieval-augmented generation (RAG) offer complementary solutions, with FT embedding domain reasoning and RAG enabling dynamic, up-to-date knowledge access. Hybrid FT + RAG frameworks have been proposed to improve factual accuracy and clinical reliability. This scoping review synthesizes current evidence on such hybrids in healthcare AI. (2) Methods: The search across PubMed, IEEE Xplore, Google Scholar, and Embase identified studies implementing explicit FT + RAG hybrids in healthcare or biomedical tasks. Eligible studies reported empirical evaluations of LLM performance or behavior. Data were extracted on base models, FT strategies, RAG architectures, applications, and performance outcomes. (3) Results: Seven studies met inclusion criteria. FT + RAG systems consistently outperformed FT-only or RAG-only approaches across QA, clinical summarization, report generation, and decision support tasks. Parameter-efficient FT methods (e.g., LoRA) were common, while RAG implementations varied (dense, hybrid, hierarchical, multimodal, federated). Reported benefits included improved accuracy, reduced hallucination, and greater clinician preference and feasibility in protected settings. (4) Conclusions: FT + RAG frameworks represent a promising direction for clinically grounded healthcare AI, combining domain-specific reasoning with transparent, up-to-date retrieval. Future work should prioritize standardized evaluation, workflow integration, and governance to enable safe deployment.