Layer-by-layer stimuli-responsive self-assembled nanocarriers for green-extracted polyphenols sequential delivery: Investigation of stability, bioactivity, and bioaccessibility during simulated gastrointestinal digestion.

This study examined university students' acceptance of generative artificial intelligence tools. For this purpose, the research was designed with a sequential explanatory model from mixed research methods. The first phase was patterned in the survey model, and the study participants consisted of 601 undergraduate students studying at a state university in Türkiye. Demographic variables, a questionnaire, and generative artificial intelligence tools, an acceptance scale, were used as data collection tools. First, assumptions were evaluated, and tests were decided to analyze the data. Within the scope of the research, Structural Equation Modelling was performed between the sub-dimensions of the acceptance scale. The case study was designed for the second phase, and interviews were conducted with 80 undergraduate students. As a result of the results, it was revealed that perceived usefulness, perceived ease of use, gender, duration of use, frequency, and purpose of use play a holistic role in shaping opinions, usage attitudes, and behavioral intentions toward generative artificial intelligence tools.

Subjective well-being is a key indicator of healthy aging. However, its relationship with family communication still requires thorough discussion. Data from the 2022 Psychology and Behaviour Investigation of Chinese Residents survey was utilized. The World Health Organization-5 Well-being Index assessed subjective well-being. The Brief Health Literacy Scale, the 10-Item Family Communication Scale, and the Brief Self-Efficacy Scale measured health literacy, family communication, and self-efficacy, respectively. A structural equation model verified path relationships. This cross-sectional study involved 2201 elderly chronic disease patients aged 60 and above. Initially, multiple collinearity tests and common method analysis were conducted, followed by determination of covariates through partial correlation analysis. After controlling for covariates, the results of the structural equation model showed a good fit for the sequential mediation model, with all paths being significant. The subjective well-being of the elderly chronic disease patients is positively correlated with family communication. Health literacy and self-efficacy play a chain mediating role in this relationship.

Clinical impact of cardiac ejection fraction and atrial fibrillation on elderly hemodialysis patients.

Time series forecasting remains a fundamental yet challenging task due to its inherent non-linear dynamics, inter-variable dependencies, and long-term temporal correlations. Existing approaches often struggle to jointly capture local temporal continuity and global contextual relationships, particularly under complex external influences. To overcome these limitations, we propose KALFormer, a knowledge-augmented attention learning transformer framework that integrates sequential modeling with external information fusion. KALFormer enhances spatiotemporal representation and contextual reasoning by integrating Long Short-Term Memory (LSTM) encoders, Transformer-based self-attention mechanisms, and knowledge-aware modules. Extensive experiments on six public benchmark datasets demonstrate that KALFormer achieves an average improvement of 8.4% in MSE and MAE compared with representative baseline models, highlighting its robustness, interpretability, and reliability for long-term time series forecasting. The source code is available at https://github.com/dxpython/KALFormer.

An intelligent method for detection and classification of darknet traffic using sequential model along with Adam and stochastic gradient decent optimisers

Effects of Modified Xuanbai Chengqi Decoction against secondary Streptococcus pneumoniae infection following influenza virus infection: A multi-omics analysis.

An Exploration of the Breast Milk Nutriome, Exposome and Microbiome and their Links to Early Growth in Preterm Infants.

Time series forecasting remains a fundamental yet challenging task due to its inherent non-linear dynamics, inter-variable dependencies, and long-term temporal correlations. Existing approaches often struggle to jointly capture local temporal continuity and global contextual relationships, particularly under complex external influences. To overcome these limitations, we propose KALFormer, a knowledge-augmented attention learning transformer framework that integrates sequential modeling with external information fusion. KALFormer enhances spatiotemporal representation and contextual reasoning by integrating Long Short-Term Memory (LSTM) encoders, Transformer-based self-attention mechanisms, and knowledge-aware modules. Extensive experiments on six public benchmark datasets demonstrate that KALFormer achieves an average improvement of 8.4% in MSE and MAE compared with representative baseline models, highlighting its robustness, interpretability, and reliability for long-term time series forecasting. The source code is available at https://github.com/dxpython/KALFormer.

Sequential Model Averaging-Based Decentralized Learning for Low Overhead and Fast Convergence

ABSTRACT The complex refraction and reflection of seismic waves through heterogeneous soil layers introduce significant stochasticity, necessitating advanced techniques for accurate seismic hazard analysis. Predicting surface-level ground motion intensity measures (IMs) is crucial for geotechnical, structural, and earthquake engineering applications, including probabilistic seismic hazard analysis (PSHA). This study presents a novel end-to-end framework using long short-term memory (LSTM) recurrent neural networks (RNNs) to predict 40 surface IMs including amplitude, frequency, duration, and energy, using earthquake source and site parameters. The framework comprises two sequential models: (i) E2B, predicting borehole-level IMs from source parameters, and (ii) EB2S, predicting surface-level IMs using both source parameters and E2B outputs. This composite approach is benchmarked against a direct baseline (E2S) model which predicts surface IMs directly from source and site parameters. A robust Japanese dataset of over 2,600 surface–borehole IM pairs is used for training and validation. Two feature selection strategies are evaluated: a physics-derived (PD) set and a data-driven (DD) set based on random forest importance. The DD EB2S model shows up to 30% coefficient of determination ( R 2 ) for short-period IMs (e.g. PGA ) and maintains high accuracy (test R 2 > 0.9 ) for long-period IMs. The PD EB2S model also improves upon PD E2S, though with smaller gains (typically 2–6%). The DD EB2S model also better preserves inter-IM correlations, offering a scalable and interpretable tool for IM prediction.

ABSTRACT Chronic Kidney Disease (CKD) continues to be an epidemic despite breakthroughs in clinical treatment and medical care. Researchers globally interested in creating high-performance screening, treatment, and preventative therapy techniques because of rise in CKD in recent. Along with the clinical evaluation, healthcare providers forecast the illness at its earliest stages by analysing the individual’s health information. To construct intelligent systems predicts CKD by analysing healthcare information, the performance of these methods requires further work to be improved. An intelligent classification and forecasting framework predicts kidney-related disorders using an enhanced Progressive Network System (PNets) which is a hybridisation of Sequential Progressive Network (SPNets) and Sequential Progressive Temporal Network (SPTNet) classification algorithm. The analysis of the proposed framework recognise CKD with 98.5% accuracy. Analysing the outcomes reveals that applying novel DL algorithms can assist with medical decision-making and early CKD and associated phase estimation, which slows the course of kidney disease.

Intimate partner violence (IPV) victimization is associated with insecure attachment and psychological distress, which can have profound implications for parenting. Yet, studies are needed to better understand the intergenerational implications of IPV, especially in non-Western countries. This study examines how experiences of IPV in grandmothers may have cascading effects on maternal IPV and mental health, with negative implications for children's attachment in Cameroon. Fifty-four families completed the study materials including validated questionnaires (grandmothers, mothers) and an attachment story completion task (preschool-aged children). Sequential mediation models were tested. The mediation was only significant for disorganized attachment. Grandmothers' IPV victimization was associated with greater scores of disorganized attachment in children (c = 0.16; 95% CI [0.03, 0.28]), via its association with increased IPV victimization in mothers, which was in turn associated with increased maternal distress (c2 = 0.05; [0.00, 0.11]). This study provides unique insights into attachment representations of Cameroonian children in the context of intergenerational IPV. Intervention on IPV and maternal mental health may improve child and family well-being.

With the proliferation of digital media, pathological gaming behavior among junior high school students has become a growing social concern. From the perspective of academic stress, this study explores its relationship with pathological gaming behavior, with a specific focus on the sequential mediating roles of emotional regulation difficulties and escape motivation. A questionnaire survey was conducted among 650 junior high school students using the Academic Stress Scale, Difficulties in Emotion Regulation Scale, Gaming Escape Motivation Scale, and Pathological Gaming Behavior Scale. The results indicated that: (1) Academic stress, emotional regulation difficulties, escape motivation, and pathological gaming behavior were all significantly positively correlated with each other. (2) Academic stress could not only directly predict pathological gaming behavior but also influence it through three indirect pathways: the separate mediating effect of emotional regulation difficulties, the separate mediating effect of escape motivation, and the sequential mediating effect of emotional regulation difficulties → escape motivation. This study constructs a sequential mediation model, revealing the influencing pathway of "academic stress → emotional regulation difficulties → escape motivation → pathological gaming behavior," providing a theoretical basis and practical insights for understanding and intervening in excessive gaming among junior high school students.

Background: This study examined how borderline personality traits influence insomnia severity.Methods: Self-report data from 230 adult women were included in the final analysis, to assess borderline personality traits, cognitive emotion regulation, pre-sleep arousal, insomnia severity. Sequential mediation analysis was conducted.Results: The sequential mediation of maladaptive cognitive emotion regulation and pre-sleep arousal in the relationship between borderline personality traits and insomnia severity was supported.Conclusions: Individuals with higher borderline personality traits use cognitive emotion regulation strategies that amplify negative emotions during everyday stressful situations, which, in turn, elevate pre-sleep arousal and contribute to greater insomnia severity. From a therapeutic perspective, interventions targeting maladaptive emotion regulation strategies used in response to daily stress, alongside techniques aimed at reducing pre-sleep arousal (e.g., relaxation training), may be particularly beneficial for alleviating insomnia symptoms in this population.

The accelerating pace of digital transformation is reshaping the foundations of firm internationalisation, challenging traditional sequential models and enabling new, non-linear paths of global expansion. This article synthesises theoretical and empirical perspectives to show how digitalisation capabilities, dynamic capabilities, digital competences, digital maturity and platform capabilities act as key enablers of internationalisation. Drawing on the Resource-Based View (RBV), the Knowledge-Based View (KBV) and dynamic capabilities theory, the article highlights how firms integrate digital technologies with organisational processes, learning mechanisms and entrepreneurial orientation in order to identify, seize and reconfigure opportunities in international markets. Digitalisation not only reduces informational and coordination barriers but also strengthens risk management, access to global knowledge and scalability.

Accurate Remaining Useful Life (RUL) prediction for Lithium-ion batteries is critical for system safety, yet its efficacy is frequently limited by data scarcity in industrial contexts. The robustness of hybrid architectures combining Convolutional Neural Networks (CNNs) with sequential models, a potential solution, has not been systematically evaluated. This study addresses this knowledge gap by first using a CNN to derive low-dimensional feature representations from full charge-discharge cycles. We then systematically assess the performance of five prominent sequential models (GRU, LSTM, Transformer, Neural ODE, and Transformer (Pre-LN)) on these features under progressively severe data scarcity (0%, 10%, 30%, and 50% cycle removal). Based on leave-one-out cross-validation on the NASA and CALCE datasets, the analysis demonstrates that the CNN-based feature extraction significantly enhances the robustness of all tested sequential models. Furthermore, recurrent networks such as GRU and LSTM, possessing strong sequential inductive biases, consistently outperform more complex architectures under data-constrained conditions. This research validates a robust predictive methodology and provides practical insights for developing reliable RUL predictors for industrial applications where data is sparse.

Course recommendation systems serve as a critical component of online education platforms, playing a vital role in enhancing learning efficiency and personalized experiences. However, existing recommendation approaches, including recent sequential models such as BERT4Rec and LightSANs, primarily concentrate on temporal-domain modeling of user behaviors while neglecting the potential of frequency-domain analysis. This leads to incomplete characterization of user behavior patterns, particularly presenting challenges in capturing stable long-term interests from sparse and noisy interaction data. To address these limitations, this study proposes a novel hybrid attention network for Massive Open Online Courses Course recommendation, designed to jointly model both frequency-domain and temporal-domain features. The model employs Fast Fourier Transform to extract frequency-domain characteristics from user behavior sequences while utilizing a self-attention mechanism to capture temporal dynamics, thereby enabling collaborative modeling of dual-domain features. Experimental results on the public MooCCube dataset demonstrate that the proposed model achieves Hit Ratio@10, MRR@10, and NDCG@10 scores of 0.4534, 0.2018, and 0.2618, respectively, outperforming current mainstream recommendation algorithms. Ablation studies further validate the effectiveness of dual-domain fusion, with approximately 10% and 5% performance improvements in NDCG@10 and Hit@10 compared to single-domain approaches. This research provides a novel technical pathway for overcoming performance bottlenecks in personalized course recommendation.

Gamma-ray bursts represent some of the most energetic and complex phenomena in the universe, characterized by highly variable light curves that often contain observational gaps. Reconstructing these light curves is essential for gaining deeper insight into the physical processes driving such events. This study proposes a machine learning-based framework for the reconstruction of gamma-ray burst light curves, focusing specifically on the plateau phase observed in X-ray data. The analysis compares the performance of three sequential modeling approaches: a bidirectional recurrent neural network, a gated recurrent architecture, and a convolutional model designed for temporal data. The findings of this study indicate that the Bidirectional Gated Recurrent Unit model showed the best predictive accuracy among the evaluated models across all gamma-ray burst types, as measured by Mean Absolute Error, Root Mean Square Error, and Coefficient of Determination. Notably, Bidirectional Gated Recurrent Unit exhibited enhanced capability in modeling both gradual plateau phases and abrupt transient features, including flares and breaks, particularly in complex light-curve scenarios.

Oral epithelial dysplasia (OED) is the precursor to oral squamous cell carcinoma (OSCC), but histologic grading alone lacks reproducibility and prognostic power. This study evaluates whether pattern-based p53 and p16 immunohistochemistry (IHC) can serve as alternative markers to genomic loss of heterozygosity (LOH) testing in predicting OED progression. From a previously characterized LOH cohort, 64 patients were assessed with IHC for p53 and p16 using defined abnormal staining patterns (overexpression, cytoplasmic, or null). Abnormal p53 expression occurred in 19% of cases, with 93% specificity, and was significantly associated with reduced progression-free survival (8-year PFS, 25% vs. 74%, p = 0.0011). Abnormal p16 expression was observed in 56% of cases with 95% sensitivity and was significantly associated with 8-year PFS (42% vs. 96%, p < 0.0001). Combined p53/p16 abnormal IHCs identified 95% of the progressing lesions and yielded superior risk discrimination (log-rank p < 0.0001), particularly at the 3-year follow-up mark. Concordance analysis revealed moderate agreement between p16 IHC and 9p LOH (κ = 0.39) and fair agreement between p53 IHC and 17p LOH (κ = 0.21), indicating that IHC and LOH detect related but distinct molecular disruptions. Chronological evaluation of serial biopsies supported a sequential model in which p16 alteration precedes p53 alteration during malignant progression. Taken together, these findings highlight the potential of a pattern-based approach with combined p53/p16 IHC as a feasible, scalable, and clinically accessible tool to guide surveillance intensity and timely clinical intervention, thereby reducing progression risks.