Pakistan Science News API

{
    "totalArticles": 24182,
    "articles": [
        {
            "id": "0752531c0f1f3e8a265eb1321ec24910",
            "title": "Neptune's mysterious moon Nereid may be an original, study shows",
            "description": "CAPE CANAVERAL, Fla. (AP) — Neptune’s far-flung moon Nereid may be the last of the planet’s original companions that managed to survive a cosmic crash, scientists reported Wednesday.\nSixteen known mo...",
            "content": "CAPE CANAVERAL, Fla. (AP) — Neptune’s far-flung moon Nereid may be the last of the planet’s original companions that managed to survive a cosmic crash, scientists reported Wednesday.\nSixteen known moons circle Neptune, our solar system’s eighth and m... [2880 chars]",
            "url": "https://www.the-journal.com/articles/neptunes-mysterious-moon-nereid-may-be-an-original-study-shows/",
            "image": "https://imengine.public.prod.dur.navigacloud.com/?uuid=b4fb43df-f9a9-55e6-953c-8164e86fd40b&function=fit&type=preview",
            "publishedAt": "2026-05-20T18:05:00Z",
            "lang": "en",
            "source": {
                "id": "9ef0b71005ad2efe9ac5564665c5e8ae",
                "name": "Front - The Journal",
                "url": "https://www.the-journal.com"
            }
        },
        {
            "id": "2eadfe8c0f020d5febf9396bd9148653",
            "title": "SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI",
            "description": "Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset “An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021”, and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F1, F2, and D) of EEGNet were systematically varied to identify the optimal configuration. Furthermore, Shapley Additive Explanations (SHAP) analysis was performed to identify the most influential EEG channels for model prediction. The proposed approach achieves the highest accuracy of 80.5% and 82.7% without and with SMOTE, respectively. The comparative analysis showed that applying SMOTE resulted in an average performance improvement of approximately 3%. A Wilcoxon signed-rank test confirmed that this improvement was statistically significant (p < 0.05). Finally, the SHAP analysis revealed that the most informative EEG channels were located over the parieto-occipital and temporal regions, which is consistent with established neurophysiological evidence related to MWL processing. The proposed framework improves both performance and explainability in EEG-based MWL classification, representing a systematic integration of SMOTE and SHAP analysis.",
            "content": "Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and i... [1504 chars]",
            "url": "https://www.nature.com/articles/s41598-026-52330-z?error=cookies_not_supported&code=49f07efe-fe5a-4068-8871-8b562622b1ee",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-05-20T03:15:25Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        },
        {
            "id": "f00d62fae13bc4b7d5030b2a06791cb2",
            "title": "Carbon halogen bond dissociation energy predictions through automated machine learning pipeline",
            "description": "Bond dissociation energy prediction for the carbon halogen (C–X) bond is quite important in chemistry, due to range applications of C–X bond in the drug design, reaction mechanism and material sciences fields. In the present research, a robust machine learning workflow was explored, to accurately predict the bond dissociation energy values of C–X bond. For the systematic identification of the optimized LightGBM Regressor as the top performing model, the automated machine leaning (Automl) framework, the Tree Based Pipeline Optimization Tool (TPOT) was employed. Additionally, tenfold cross-validation was used to rigorously confirm the model’s robustness. The final model exhibited outstanding predictive capability, with a coefficient of determination (R2) of 0.93 on the internal test set, and 0.95 on a more stringent external validation set. Moreover, interpretation of the model via SHapley Additive exPlanations (SHAP) suggests that the model predictions are based on chemically intuitive concepts, including electronegativity difference, halogen atomic number, and local atomic charges. This work thus provides a tool for bond dissociation energy prediction that is both highly accurate and interpretable, while simultaneously demonstrating a powerful contemporary workflow for producing machine learning models that are interpretable for basic problems in chemistry.",
            "content": "Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give... [797 chars]",
            "url": "https://www.nature.com/articles/s41598-026-48010-7?error=cookies_not_supported&code=9d4aa047-2fc9-4fcd-846e-065b8b642a27",
            "image": "https://www.nature.com/static/images/favicons/nature/favicon-48x48-b52890008c.png",
            "publishedAt": "2026-05-20T02:58:06Z",
            "lang": "en",
            "source": {
                "id": "7abf0df285fbe93cdccffcc7c4088737",
                "name": "Nature",
                "url": "https://www.nature.com"
            }
        }
    ]
}