We, the publisher, are issuing an expression of concern about Jie Xu, (2024) Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training. https://doi.org/10.4108/eetpht.10.5161 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Wei Zhu (2024) Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration. https://doi.org/10.4108/eetpht.10.5152 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
INTRODUCTION: Background music is frequently incorporated into learning environments not only to enhance cognitive engagement but also to regulate learners’ affective state and perceived mental effort, both of which are closely associated with learning-related well-being. However, empirical findings regarding its impact on memory performance remain inconclusive and appear to be contingent upon task characteristics as well as the emotional properties of the auditory stimulus. Recent advances in Artificial Intelligence Generated Content (AIGC) enable the parametric synthesis of music with systematically controllable emotional attributes (e.g., valence, arousal, motivational tone), thereby providing a novel methodological pathway for examining how adaptive auditory environments may support learners’ cognitive functioning, stress regulation, and psychological well-being beyond the constraints of pre-composed music. OBJECTIVES: This study investigates how emotionally differentiated AI-generated background music influences memory performance, subjective workload, and user preference within learning contexts, with particular attention to task-dependent effects across numerical and image-based memory paradigms. METHODS: Seventeen participants completed both numerical recall and visual memory tasks under four auditory conditions: positive/motivational, soothing, focus-oriented (Low-arousal) AI-generated music, and a silence control. Behavioural accuracy was recorded alongside indicators of electroencephalography (EEG) and heart rate variability (HRV). Subjective workload and affect were assessed using the NASA Task Load Index (NASA-TLX) and the Positive and Negative Affect Schedule (PANAS). RESULTS: Results revealed dissociable, task-dependent patterns across behavioural and psychophysiological measures. Silence yielded the highest accuracy in numerical memory tasks, whereas positive-valence music was associated with enhanced performance in image memory conditions. Distinct categories of AI-generated music also elicited differential neural and autonomic responses, suggesting variations in affective regulation and cognitive load during task execution, with focus-oriented music emerging as the most preferred auditory condition for sustained learning. Although overall behavioural accuracy remained relatively stable across conditions, emotionally parameterised AI-generated music may function as autonomy-supportive cognitive scaffolds CONCLUSION: These findings provide preliminary evidence that AIGC-driven auditory environments may function as autonomy-supportive cognitive scaffolds that dynamically regulate stress and attentional demands during task engagement. By adaptively aligning emotional soundscapes with task characteristics and user state, AI-generated background music holds promise as a well-being-aware intervention capable of promoting cognitive sustainability and reducing perceived mental strain in digitally mediated learning systems.
We, the publisher, are issuing an expression of concern about Binbin Han et al. (2024) Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm. https://doi.org/10.4108/eetpht.10.5147 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
INTRODUCTION: Breast cancer remains one of the most prevalent causes of cancer-related mortality among women globally. While machine learning (ML) has demonstrated promise in early detection, conventional models often rely solely on statistical features, lacking domain-specific knowledge and interpretability. OBJECTIVES: This study aims to enhance breast cancer prediction by integrating ontology-driven semantic features with ML models to improve both predictive accuracy and clinical interpretability. METHODS: We applied a comprehensive pipeline comprising data preprocessing, statistical testing, and dimensionality reduction using PCA, followed by training with supervised learning models including Logistic Regression, k-NN, SVM, Random Forest, XGBoost, LightGBM, and Attention-Enhanced MLP. In the proposed approach, clinical data is transformed into RDF triples and structured within a domain-specific breast cancer ontology. Semantic reasoning via SPARQL queries enables the extraction of high-level features, which are then used in a leakage-safe stacking design that integrates (i) tabular features, (ii) KGE features, (iii) semantic subtyping signals, and (iv) SPARQL rule features, with reproducible templates and released code. RESULTS: Across four benchmark datasets, the ontology-enhanced meta-learner achieved consistently strong performance, achieving 0.996 ± 0.006 ROC-AUC on WDBC under stratified evaluation. CONCLUSION: Incorporating ontology-derived semantic knowledge significantly improves the performance, robustness, and interpretability of ML models for breast cancer prediction. This approach holds strong potential for real-world integration into clinical decision support systems.
We, the publisher, are issuing an expression of concern about Jing Wang (2024) Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students. https://doi.org/10.4108/eetpht.10.5506 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Sarita Saavedra et al. (2024) RAnalysis of the implementation of teletraining and teleIEC in healthcare services: Case study. https://doi.org/10.4108/eetpht.10.5057 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Naara Medina-Altamirano et al., (2024) Thermal image processing system to monitor muscle warm-up in students prior to their sports activities. https://doi.org/10.4108/eetpht.10.5888 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Shuaishuai Zhang, Gang Chen (2024) Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes. https://doi.org/10.4108/eetpht.10.5856 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Yu Li, Yuetong Gao (2024) Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status. https://doi.org/10.4108/eetpht.10.5899 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Stain normalization is a crucial pre-processing process for the accurate interpretation of Haematoxylin and Eosin (H&E) stained histopathology images based on colorectal cancer. Effective normalization improves classification accuracy by reducing computational complexity, addressing inter- variability in background colors across a dataset, and minimizing data loss. This paper proposes a novel approach that combines Reinhard normalization with a Generative Adversarial Network (ReinhardGAN) to enhance image color properties, such as consistency, contrast, and luminance. To further optimize the normalization process, a Hybrid Strategic Light Optimization (HSLO) algorithm is introduced, minimizing the loss and computational cost during the H&E-stained image normalization. The experimental results demonstrate that the proposed ReinhardGAN-HSLO provided outstanding performance over conventional color normalization methods in terms of colour consistency and normalization as indicated by qualitative and quantitative assessments
We, the publisher, are issuing an expression of concern about Fangming Dai, Zhiyong Li, (2024) Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling. https://doi.org/10.4108/eetpht.10.5907 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Gang Chen, Shuaishuai Zhang, (2024) Analysis Method of Special Physical Training Mode of Basketball Teams in Colleges Based on WeChat Applet and FTTA Optimised LSTM. https://doi.org/10.4108/eetpht.10.5853 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Antony Paul Espiritu-Martinez et al., (2024) Bibliometric analysis of publications on neuroscience and noncommunicable diseases in the Scopus database. https://doi.org/10.4108/eetpht.10.5699 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Changqing Liu, Yanan Xie (2024) Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training. https://doi.org/10.4108/eetpht.10.5763 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Diandong Lian (2024) Deep learning in sports skill learning: a case study and performance evaluation. https://doi.org/10.4108/eetpht.10.5809 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Bin Wu (2024) Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes. https://doi.org/10.4108/eetpht.10.5150 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Espinoza-Véliz et al. (2024) Status of high-impact scientific publication in nursing in Latin America. https://doi.org/10.4108/eetpht.10.5705 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Yunxiang Shang (2024) Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology. https://doi.org/10.4108/eetpht.10.5138 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
We, the publisher, are issuing an expression of concern about Lenka Angelita Kolevic Roca et al. (2024) Analysis of the use of electronic medical records and its effect on improving patient care. https://doi.org/10.4108/eetpht.10.5702 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
