The prediction of protein-protein interaction (PPI) can be insightful for exploring the molecular mechanisms of cellular functions. Constructing the negative datasets of PPI is related to the assessment of the prediction accuracy and evaluation of the prediction performance. Aiming at the problem of unstable prediction accuracy in the current method of building negative sets using random sampling, we proposed a method of constructing negative sets based on a conditional generative adversarial network (CGAN), named PPIGAN. This method generates negative samples through a generative network, and the PPI prediction model uses these generated negative samples along with positive samples to learn interaction features. Simultaneously, the generator and the prediction model continuously compete against each other during the learning process, which enhances the model's generalization ability and prediction accuracy. Experimental results show that the accuracy of our proposed method reaches 94.68% and 98.22% in 5-fold cross-validation on yeast and human datasets, respectively. These results either surpass or closely approach the performance of advanced PPI prediction models such as PIPR, convolutional neural network, DeepTrio, and DeepFE, indicating that the method proposed in this article provides an effective solution for the work related to PPI prediction.

Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.

Tailoring instruction to personality: The mediating role of cognitive tendencies in the effect of extraversion on higher vocational college students' self-regulated learning.

Using real-time process data of domain-specific learning processes to provide adaptive support for learning and instruction: Challenges and opportunities

Uncovering video-based learning processes: Cued-retrospective reporting versus concurrent and retrospective reporting

Multiple interpretation ensemble distillation for graph neural networks.

Interest in, and the need for, remote learning tools and techniques, as well as the adoption and deployment of digital technologies for tutoring and training, continues to rise. Concurrently, over the past decade, the use of Virtual Reality (VR) applications as a supplementary tool in the learning process has increased significantly. Therefore, VR simulation applications are expected to play a key role in people's education and training in the coming years. In this study, we designed and developed a VR training application called ViRtus -a VR-based system following constructivist and serious game human-computer interaction principles for VR-aided training- and applied it to a real-world scenario, i.e., the construction of an industrial electrical control panel. To demonstrate the potential and sustainability of the VR training system as an alternative to traditional apprenticeship training procedures, we conducted an experimental study comparing the real-world outcomes of three groups trained using a conventional method, a VR-only training, and a hybrid approach combining both. Based on qualitative assessments and the statistical analysis of practical experiments, the collected data and observations indicate that the hybrid VR constructivist training strategy -supplementing conventional trainer instructions with the VR application- can enhance training effectiveness and promote a more productive and sustainable workplace, for both trainees and trainers, in high-risk industrial tasks.

Exposure to sexual stimuli induces specific changes of dendritic spine in the prefrontal and accumbens regions of naïve and sexually-experienced male rats.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8-22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher-order temporal structure. After an initial learning phase, participants' explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants' response times during learning, and found that all participants relied on simple, recency-based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events. SUMMARY: Children, adolescents, and adults all learn to segment continuous streams of structured perceptual input, but they may do so via different learning processes. We examined how the learning mechanisms that enable people to predict upcoming experiences change and relate to structured knowledge acquisition across development. Computational modeling revealed that in a graph-learning task, younger participants relied on simple, recency-based prediction, while older participants tracked temporal relations between stimuli. The extent to which participants engaged in this sophisticated form of predictive learning only weakly related to their knowledge of the task's structure.

Firebrand transport is a primary cause of fire spread in outdoor fires, but its prediction is challenging. This study develops a highly accurate and low-cost prediction model for firebrand transport using machine learning based on experimental data. Experimental dataset used is firebrand data from wood materials of single size and produced from a firebrand generator. First, five machine learning models were built and compared. The results showed that a Neural Network (NN) provided the best performance, accurately reproducing the landing distribution of firebrands. Next, SHAP analysis were used and it was found that physical indices, such as the Tachikawa number, were critical for accurate predictions. Based on this finding, a Physics-Constrained Neural Network (PCNN), which incorporates physical laws into the learning process, was developed. The PCNN model was tested against unseen data which is firebrand data generated from combustion experiments with roof assemblies. The PCNN, especially when constrained by the Tachikawa number, demonstrated more robust and accurate predictions on unseen, more complicated data compared to the standard NN. The ML technique developed in this study is able to predict firebrand transport with different shape. This work shows that machine learning, particularly PCNN, is a powerful tool for predicting complex firebrand transport, potentially contributing to future fire risk assessment. • NN proven to be superior for firebrand transport prediction to other four ML model • SHAP analysis identified Ta , Re , and velocity ratio as key physical features • PCNN showed improved generalization performance on unseen, complicated data • Ta as physical constraint was the most effective approach for improving PCNN

<p>The need to produce graduates who are responsible global citizens endowed with values, skills, knowledge, and competencies has increased recently. This qualitative study examined strategies for enhancing the teaching of history for global citizenship education (GCED) by Zimbabwean high school history teachers, using a single-case study design. Drawing from transformative learning theory (TLT), the study was conducted in five high schools in Zimbabwe, selected through purposive sampling, with ten teachers conveniently sampled. The study gathered data through document analysis, lesson observations, and semi-structured interviews, and analyzed it using a thematic approach based on Braun and Clarke’s six steps. Key findings of this study reveal that teachers are using several strategies to enhance the teaching of history for GCED, including an experiential and participatory approach that involves learners actively in the learning process, such as through dramatization, interactive teaching, and facilitation. The study highlights an urgent need for policymakers, particularly in Zimbabwe and those responsible for the GCED framework, to develop tools that protect teachers, enabling them to teach GCED concepts despite sensitivities or controversies effectively. Furthermore, it recommends that the Zimbabwean government and other similar contexts provide more support to teachers, as the learner-centered teaching approaches require modern teaching equipment, which is more effective than just question-and-answer techniques. The study offers insights into how to adopt the GCED framework and effective GCED teaching strategies. </p>

Broadband, as a high-speed Internet connectivity, is an essential enabler for digital preparedness. However, little is known, in the case of Tanzania, about the nexus between broadband diffusion and digital preparedness, particularly in the facilitation of the teaching and learning process. This study, therefore, aimed at investigating the nexus between broadband diffusion and digital preparedness in Tanzania by focusing on the country’s readiness to leverage the same for formal education. The study employed a systematic review, guided by the PRISMA framework, to obtain the empirical evidence from existing literature. Thematic analysis was used to identify themes, analyze, and interpret qualitative data from the reviewed literature. Findings show that, although Tanzania instituted several policies and initiatives regarding broadband diffusion, it still missed a few key policy documents that would help to foster the rollout of broadband. In addition, legal instruments tailored to promote and support broadband diffusion were still lacking. Furthermore, more infrastructural improvements and support for digital literacy were needed. Based on the study’s findings, a conclusion has been made that Tanzania still faces considerable work ahead to effectively leverage digital technologies in formal education. Among other suggestions, the study recommended that relevant policies on broadband diffusion should be established and operationalized throughout the country.

Nurse educators and professional development specialists must be innovative and engaging in facilitating nurse learning and competency validation. Facilitating learning requires a shift toward creative, interactive methods, especially given a multigenerational workforce with diverse learning styles. The use of gamification, adult learning principles, and a variety of validation methods, while incorporating a creative theme, fosters the learning and competency validation process.

Drug-drug interaction (DDI) prediction remains a significant challenge due to the complexity of biological systems and the growing demand for precise predictions. Recent advances in deep learning have been successfully applied to DDI prediction. However, the asymmetrical nature of DDIs is always neglected, which can lead to some information loss during the feature learning process. To address the issue, a novel DDI prediction method based on knowledge graph and generative adversarial network, KGGAN-DDI, is proposed to predict potential DDIs. First, a knowledge graph embedding module is designed to capture and encode asymmetric associations between drug pairs, which can enhance the feature representation and contextual relevance of drug interactions. Then, a dual-generator GAN is adopted to produce realistic samples and improve the prediction accuracy further. Moreover, a least squares loss function is utilized to mitigate the vanishing gradient problem, thereby providing smoother gradients and enhancing the efficiency and stability of the optimization process. Extensive experiments demonstrate that the proposed KGGAN-DDI performs better than state-of-the-art methods. Case study further shows the effectiveness of KGGAN-DDI.

The association between generative artificial intelligence use and foreign language classroom anxiety.

Framework for hierarchical deep reinforcement learning with conceptual embedding.

Multi-view graph clustering via dual attention fusion and collaborative optimization.

Structure-missing graph-level clustering network.

The task of surface reconstruction from point clouds is to produce high-quality meshes using sampled 3D points (no images available). Traditional methods primarily focus on geometric accuracy but often produce meshes without texture colors. In this paper, we present a brand-new perspective in point cloud reconstruction task-Imagining points as more informative Gaussian splats and obtaining colored surfaces through free-form Gaussian-rendering reconstruction. We train a universal Point-to-Gaussian model to infer the attributes of Gaussian splats for any given pointcloud with merely point coordinates (and color optionally) as input, without requiring any image. Significant technical designs are applied on initialization, regularization and loss functions, making the whole learning process stable. The inferred Gaussian splats can faithfully recover the original appearance of objects or scenes (capable of quick rendering from any viewpoint, like human's imagination ability), meanwhile closely adhering to the input shape. After obtaining a sufficient number of virtually rendered images and depth maps, we employ the truncated signed distance function (TSDF) fusion to get the reconstruction results, producing high-quality and colored meshes. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods in surface reconstruction metrics while maintaining high efficiency and simplicity.

Educational evaluation is an essential part of the learning process, serving to measure the achievement of educational goals and to continuously improve the quality of learning. This study aims to analyze educational evaluation theory in learning, including the basic concepts of evaluation, evaluation models and approaches, and their relevance to 21st-century learning. The study employed a qualitative approach with the Systematic Literature Review (SLR) method through a review of various reputable international journals published between 2020 and 2025. Research data was obtained from various scientific sources such as Scopus, ScienceDirect, Springer, Taylor & Francis, and Sage Journals. The results show that educational evaluation theory is evolving from outcome-oriented evaluation to a more holistic, authentic, and competency-based evaluation. Evaluation models such as Goal-Oriented Evaluation, formative and summative evaluation, the CIPP model, authentic assessment, and diagnostic assessment play an important role in supporting modern learning. In addition, the integration of digital technology in learning evaluation provides opportunities to create a more adaptive and contextual assessment system.