Browse Articles

Large language models for prognostic analysis in mechanical fault diagnosis

With the in-depth development of industrial intelligence, as the core basic component of high-end equipment, the fault diagnosis and health management of rotating machinery has become a key link to ensure the reliability of complex systems. Although the intelligent diagnosis technology based on mechanical vibration signals has made remarkable progress, in complex mechanical systems, it is difficult to comprehensively cover the fault feature space using vibration signal data only.This paper proposes an intelligent diagnosis framework based on a large language model. By empowering the large language model through multimodal data feature fusion and constructing a ternary data system of “raw vibration signals - time-frequency spectrum features - fault knowledge text”, the framework realizes cross-modal joint representation of mechanical fault features and breaks through the bottlenecks of traditional methods, such as insufficient feature extraction capability under complex working conditions and limited cross-scenario generalization. The framework innovatively integrates the deep semantic understanding ability of pre-trained large language models with mechanical fault mechanisms. Through the method of plugging in principle knowledge bases, the model can not only output fault location results but also simultaneously generate interpretable reports including fault cause analysis and maintenance strategy suggestions.The model proposed in this paper has been strictly tested on bearing datasets. Experimental results demonstrate that the model exhibits excellent performance and adaptability in different industrial scenarios.

Association between intraocular pressure and climate parameters

Situational motionless camouflage of a loliginid squid

Short-term impact of preschool sound exposure on outer hair cell function in young children: An analysis using pressurised distortion product otoacoustic emissions

Background Preschool children are regularly exposed to high noise levels that may affect hearing. A previous study has linked preschool noise exposure to reduced distortion product otoacoustic emission (DPOAE) amplitudes. Since DPOAEs primarily reflect outer hair cell (OHC) activity, they provide an indirect marker of cochlear function. Measurement accuracy can be affected by middle-ear pressure. Pressurised DPOAEs (pDPOAEs) compensate for middle-ear pressure during recording. Methods This cross-sectional study aimed to examine the relationship between preschool noise exposure and pDPOAE amplitudes while accounting for middle-ear pressure. Seventy-five children (4–6 years old) were monitored using dosimeters to measure the equivalent continuous sound level (L AeqTi) and the 95th percentile of maximum levels (L AFmax,95). Of these, 56 children completed pDPOAE testing at four time points during the preschool week. Linear mixed-effects models evaluated associations with noise exposure, time of day and progression across the week. Results For personal dosimetry, the mean L AeqTi was 80 dB (range: 60–98 dB) and the mean L AFmax,95 was 97 dB (range: 77–110 dB). Most L AeqTi levels (86.2%) were between 75–85 dB, with L AFmax levels exceeding 115 dB in 53.8% of the cases. No significant associations were found between L AeqTi or L AFmax,95 and pDPOAE amplitudes (p > 0.05). Time-of-day differences were observed, with higher amplitudes in the afternoon at 4 kHz (p = 0.045) and 6 kHz (p = 0.047) in the right ear, and 3 kHz in the left ear (p = 0.021). Girls showed higher amplitudes than boys at 4 kHz in the left ear (p = 0.030). Conclusions Although pDPOAE amplitudes varied with time of day, and sex, a direct exposure–response relationship with preschool noise was not demonstrated. Short-term variations in typical preschool noise exposure may not measurably affect cochlear function in young children. Future research should refine exposure assessment and recording protocols to reduce variability and improve detection of small physiological changes.

Independent associations of physical activity and depression with open-angle glaucoma in a population-based analysis

Abstract Patients with open-angle glaucoma (OAG), a chronic visual impairment, are at elevated risk of developing depression. Physical activity (PA) has been associated with benefits for mental health, but evidence is limited regarding its impact on depression risk in individuals with OAG. This study aimed to assess the association between PA and incident depression in a nationwide cohort of patients newly diagnosed with OAG. We conducted a retrospective cohort study using customized data from the Korean National Health Insurance Service (KNHIS). The cohort included 97,617 individuals newly diagnosed with OAG between 2011 and 2015, with follow-up until depression onset, death, or study end in 2023. Age- and sex-matched controls without OAG were selected at a 1:2 ratio. PA levels were classified as low or high, based on validated self-report, and categorized into four patterns according to change before and after OAG diagnosis: consistently low, consistently high, increased (low to high), and decreased (high to low). The primary outcome was incident depression identified using ICD-10 diagnostic codes. Multivariable Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for depression risk according to PA patterns. Among patients with OAG (mean [SD] age, 59.7 [12.5] years; 42.5% female), those maintaining high PA levels after diagnosis had a lower risk of developing depression compared with those with low PA (HR, 0.877; 95% CI, 0.856–0.900). The most favorable outcomes was observed in patients with consistently high PA (HR, 0.855; 95% CI, 0.829–0.883), followed by those who increased their PA post-diagnosis (HR, 0.912; 95% CI, 0.879–0.947). Patients who decreased PA or remained inactive showed no significant reduction in depression risk. Overall, Higher or increasing physical activity after OAG diagnosis was associated with a modest but meaningful reduction in depression risk. These findings suggest that encouraging PA may help mitigate depression risk and should be considered as part of comprehensive glaucoma care.

A multilevel Bayesian approach to climate-fueled migration and conflict

Abstract Do climate conditions and extreme events fuel conflict and migration? This question has been widely studied using causal designs that exploit natural variation in climate variables, often analyzed with linear fixed-effects models. Yet in this setting, nonlinear relationships, distributional features of outcomes, and spatial heterogeneity can cause these models to violate core assumptions and yield unreliable inferences. We propose a multilevel Bayesian framework that accommodates such features while retaining identification strategies from natural experiments. We illustrate its potential with a representative analysis from the literature of the effect of temperature anomalies on conflict in Somalia. When outcome distributions suited to event counts are combined with partial pooling across regions, the apparent aggregate climate effect disappears and marked regional heterogeneity emerges, with positive associations in only a few southern regions and negative or uncertain effects elsewhere. Extending pooling across time further improves predictive ability. More broadly, the multilevel Bayesian framework offers a general strategy for strengthening both explanatory and predictive inferences about climate and social outcomes, supporting internal and external validity while efficiently accommodating heterogeneity even with small samples. This methodological bridge between econometric identification strategies and statistical modeling provides a robust foundation for interdisciplinary climate-conflict-migration research.

Circulating microRNAs as biomarkers for diabetic retinopathy stage identification: A DTA systematic review and meta-analysis

Purpose To evaluate the diagnostic accuracy of circulating miRNAs in distinguishing between different diabetic retinopathy (DR) stages in type 2 diabetes mellitus (T2DM). Methods We conducted a systematic review and meta-analysis in accordance with PRISMA-DTA and Cochrane guidelines. The protocol was not registeres and no external funding was received. A comprehensive search was performed in PubMed, CENTRAL, Scopus, Web of Science, ScienceDirect, and ClinicalTrials (up to January 2025) to identify diagnostic test accuracy studies on circulating miRNAs for DR. Eligible studies included three predefined comparisons: healthy controls versus DR (CTL vs DR), T2DM without DR versus DR (T2DM vs DR), and non-proliferative versus proliferative DR (NPDR vs PDR). DR diagnosis was confirmed using fundus fluorescein angiography and/or fundus examination. Two reviewers independently conducted study selection, data extraction, and risk of bias assessment with QUADAS-2; certainty of evidence was assessed using GRADE. Data were synthesized using a bivariate random-effects meta-analysis, with subgroup analyses, meta-regression, and sensitivity analyses to explore heterogeneity. Data were synthesized via a bivariate random-effects meta-analysis, with subgroup analyses, meta-regression, and sensitivity tests to explore heterogeneity. Results Sixteen studies (1849 participants; 21 miRNAs) were included. For CTL vs DR (7 studies), pooled sensitivity was 77% (70–82) and specificity 84% (77–89), AUC 0.86 (0.82–0.89). For T2DM vs DR (9 studies), sensitivity was 81% (75–86) and specificity 80% (71–87), AUC 0.88 (0.84–0.91). For NPDR vs PDR (12 studies), sensitivity was 84% (79–87) and specificity 82% (76–88), AUC 0.90 (0.87–0.93). Heterogeneity arose chiefly from sample matrix, normalization strategies and inter-study expression trends. Patient selection posed the greatest bias risk. Conclusions Circulating miRNAs exhibit promising diagnostic accuracy for differentiating among various stages of DR. However, future large, prospective studies in diverse populations and standardized pre-analytical protocols are required to confirm and translate these findings.

RETRACTED ARTICLE: A novel malignant mesothelioma organoids-T cell co-culture platform for personalized immunochemotherapy testing

Visually detectable facial mimicry in response to android facial expressions

Advanced electrochemical biosensing of pathogens: Harnessing the antimicrobial properties of Ib-M peptides for highly sensitive bacterial detection

This study describes the development of electrochemical biosensors with high sensitivity to detect pathogenic bacteria, including Escherichia coli O157:H7, Pseudomonas aeruginosa , and Staphylococcus aureus , in aqueous environments. The biosensors employ the antimicrobial peptides Ib-M1 and Ib-M6 as biorecognition elements, immobilized on gold nanoparticle-modified screen-printed electrodes via a self-assembled monolayer. Detection was achieved through electrochemical impedance spectroscopy, achieving remarkably low limits of detection of 1.4 CFU/mL for E. coli O157:H7 and S. aureus , and 0.8 CFU/mL for P. aeruginosa . The biosensors exhibited linear detection ranges of 0–100 CFU/mL for E. coli O157:H7 and S. aureus , and 0–75 CFU/mL for P. aeruginosa . Notably, the incorporation of carbon nanotubes significantly improved analytical sensitivity of the biosensors, particularly for E. coli O157:H7 and S. aureus . These results highlight the potential of the proposed biosensors for rapid, on-site monitoring of microbial contamination in drinking water, food processing environments, and clinical settings.

Alternative social and reproductive niches linked to intra-sexual color variation in a facultatively protogynous North American annual killifish, Millerichthys robustus

Electrochemical sensor based on Co3O4 and Au nanoparticles for simultaneous determination of As3+ and Hg2+ by stripping voltammetry

Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation

Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing this random sampling with a personalized query. Bayesian optimization, a sample-efficient nonlinear optimization method, is the gold standard for human-in-the-loop optimization problems, such as finding this query. In this paper, we present random rotational embedding Bayesian optimization (ROMBO). This novel method can efficiently sample and optimize high-dimensional spaces with rotational symmetries, like the Gaussian latent spaces found in generative models. ROMBO works by embedding a low-dimensional Gaussian search space into a high-dimensional one through random rotations. Our method outperforms several baselines, including other high-dimensional Bayesian optimization variants. We evaluate our algorithm through a music generation task. Our evaluation includes both simulated experiments and real user feedback. Our results show that ROMBO can perform efficient personalization of a generative deep learning model. The main contributions of our paper are: we introduce a novel embedding strategy for Bayesian optimization in high-dimensional Gaussian sample spaces; achieve a consistently better performance throughout optimization with respect to baselines, with a final loss reduction of 16%-31% in simulation; and complement our simulated evaluations with a study with human volunteers (n = 16). Users working with our music generation pipeline find new favorite pieces 40% more often, 16% faster, and spend 18% less time on pieces they dislike than when randomly querying the model. These results, along with a final survey, demonstrate great performance and satisfaction, even among users with particular tastes.

Efficacy of Nigella sativa L. and Trigonella foenum-graecum straw ethanolic extracts against some tomato pests in a greenhouse

Abstract The tomato ( Lycopersicon esculentum ) is a crucial vegetable crop worldwide, but various pests threaten its yield. Excess food and agricultural waste create health and environmental issues. This study evaluated the pesticidal activity of ethanolic extracts from Nigella sativa and Trigonella foenum-graecum straw ethanolic extracts against Amrasca biguttula biguttula , Liriomyza trifolii , and Tuta absoluta under greenhouse conditions. Tomato leaflets were collected from treated plots before spraying and examined in the lab for pests. Then, infestation rates were assessed 7 days after spraying by comparing the number of larvae to that of an untreated control. All treatments showed significant differences in the mean number of recorded pest infestations after the first and second sprays compared to the control. The 5% T. foenum-graecum extract was the most effective, reducing pests by 78.98%, 81.94%, and 28.03%, respectively, while N. sativa extract caused an 88.75% reduction in A. biguttula . The high-performance liquid chromatography analysis identified 18 phenolic compounds in N. sativa straw extract, with the predominance of catechol (330.14 µg/mL), chlorogenic acid (169.23 µg/mL), catechin (94.07 µg/mL), naringenin (91.99 µg/mL), and rutin (78.16 µg/mL). A similar profile was observed for the ethanolic extract of T. foenum-graecum straw, with some quantitative differences, where ellagic acid (287.13 µg/mL), gallic acid (188.89 µg/mL), naringenin (48.71 µg/mL), rutin (34.99 µg/mL), and catechin (33.97 µg/mL) were the major phenolics in the extract. In line with the above findings, rutin, chlorogenic acid, and daidzein showed the highest in-silico docking scores against AChE, GABA (A) , and RyR enzymes compared to the controls. These results suggest that agricultural waste from N. sativa and T. foenum-graecum can serve as novel, environmentally friendly bioinsecticides.

Effects of ZnO nanoparticles on mortality and growth performance in broiler chickens

Inhibition of NF-κB pathways alleviates hydrocephalus via modulation of choroid plexus epithelium inflammation in a rat intraventricular hemorrhage model

Backgrounds Post-hemorrhagic hydrocephalus (PHH) is a serious complication following hemorrhagic events due to cerebrospinal fluid (CSF) pathway disorders. We explore the Nuclear Factor κB (NF-κB) signaling pathway’s involvement in choroid plexus epitheliums (CPEs) inflammation and hydrocephalus, aiming to identify new therapeutic targets for managing PHH. Materials and Methods Adult male Sprague-Dawley rats were used to establish an intraventricular hemorrhage (IVH) autologous-blood model. Rats were randomly assigned to four groups: artificial cerebrospinal fluid (aCSF), IVH, IVH + TNF-α inhibitor, and IVH + NF-κB inhibitor. CSF secretion rates, lateral ventricular volumes, and inflammatory cytokine levels in CSF were measured at 3, 7, and 14 days post-modeling. Western blotting and immunofluorescence were used to analyze NF-κB pathway activation and the related inflammatory markers, including NF-κB, TNF-α, Illinois, Na+-K+-Cl− co-transporter 1 (NKCC1), aquaporin-1 (AQP1), and aquaporin-4 (AQP4), Results TNF-α and NF-κB inhibitors effectively reduce lateral ventricular enlargement and CSF secretion rates following IVH in rats. The concentration of TNF-α in the IVH group was significantly higher than in the aCSF group as well as the two inhibitor groups. On days 3 and 7 post-modeling, Western blot and immunofluorescence analyses revealed altered expression of pNF-κB (p65), and proteins in CPEs across groups, with TNF-α and NF-κB inhibition reducing pNF-κB and levels. Illinois and NKCC1 changes were tissue, Conclusions NF-κB activation post-IVH drives CPEs inflammation, increases CSF production, and contributes to hydrocephalus formation. Targeting the NF-κB pathway offers a promising strategy for the treatment of PHH.

Audio and video nearables for monitoring respiratory rate in sleeping dogs

Neurons of the precuneiform nucleus share structural and functional properties of the mesencephalic locomotor region

Exploring the link between AI usage intention and digital competence among college PE teachers: A moderated mediation model based on SCT and UTAUT

As artificial intelligence (AI) technologies rapidly integrate into higher education, they impose increasing demands on the teaching approaches and digital competence of physical education teachers. However, the relationship between physical education teachers’ behavioral intention to use AI and their digital competence remains underexplored. This study focuses on college physical education teachers and examines the relationship between their intention to use AI and their digital competence. Grounded in Social Cognitive Theory (SCT) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes a structural equation model incorporating behavioral intention, self-efficacy, social influence, and digital competence, with gender as a moderating variable. A questionnaire survey was conducted among 479 physical education teachers from ten universities in mainland China, and the model was tested using AMOS and SPSS. The results indicate that teachers’ behavioral intention to use AI is positively associated with their self-efficacy, perceived social influence, and digital competence, with both self-efficacy and social influence serving as significant mediators. Furthermore, self-efficacy is positively related to social influence, while gender does not exert a significant moderating effect on any of the proposed paths. This study contributes to the integrated application of SCT and UTAUT in the context of physical education in higher education and offers theoretical and practical implications for enhancing digital competence and promoting intelligent transformation among college physical education teachers.

Lung tissue T cells are activated in chronic obstructive pulmonary disease with non-small cell lung cancer patients