Category: 3. Business

  • Chen, C., Isa, N. A. M. & Liu, X. A review of convolutional neural network-based methods for medical image classification. Comput. Biol. Med. 185, 109507. https://doi.org/10.1016/j.compbiomed.2024.109507 (2025).

    Google Scholar 

  • Yu, M., Xu, Z. & Lukasiewicz, T. A general survey on medical image super-resolution via deep learning. Comput. Biol. Med. 193, 110345 (2025).

    Google Scholar 

  • Jia, Y., Dong, L. & Jiao, Y. Medical image classification based on contour processing attention mechanism. Comput. Biol. Med. 191, 110102 (2025).

    Google Scholar 

  • Zhong, C., Li, G., Meng, Z., Li, H. & He, W. A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput. Biol. Med. 153, 106520. https://doi.org/10.1016/j.compbiomed.2022.106520 (2023).

    Google Scholar 

  • Yue, J., Guo, Y. & Gao, H. Wrapper-based feature selection for general dataset: the quantum sand cat swarm optimization. In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT) (pp. 1–6). IEEE. (2024)

  • Haribabu, M. & Guruviah, V. FFSWOAFuse: Multi-modal medical image fusion via fermatean fuzzy set and Whale optimization algorithm. Comput. Biol. Med. 189, 109889 (2025).

    Google Scholar 

  • Krishna, T. B. & Kokil, P. Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models. Expert Syst. Appl. 238, 122153. https://doi.org/10.1016/j.eswa.2023.122153 (2024).

    Google Scholar 

  • Rahman, R. et al. Demystifying evidential dempster Shafer-based CNN architecture for fetal plane detection from 2D ultrasound images leveraging fuzzy-contrast enhancement and explainable AI. Ultrasonics 132, 107017. https://doi.org/10.1016/j.ultras.2023.107017 (2023).

    Google Scholar 

  • Sarker, M. M. K. et al. COMFormer: Classification of maternal-fetal and Brain Anatomy Using a Residual cross-covariance attention-guided Transformer in Ultrasound (IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2023).

  • Burgos-Artizzu, X. P. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci. Rep. 10 (1), 10200. https://doi.org/10.1038/s41598-020-67076-5 (2020).

    Google Scholar 

  • Rathika, S., Mahendran, K., Sudarsan, H. & Ananth, S. V. Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection. BMC Med. Imaging. 24 (1), 337. https://doi.org/10.1186/s12880-024-01453-8 (2024).

    Google Scholar 

  • Rauf, F. et al. Automated deep bottleneck residual 82-layered architecture with bayesian optimization for the classification of brain and common maternal fetal ultrasound planes. Front. Med. 10, 1330218. https://doi.org/10.3389/fmed.2023.1330218 (2023).

    Google Scholar 

  • Al-Razgan, M., Ali, Y. A. & Awwad, E. M. Enhancing fetal medical image analysis through attention-guided convolution: A comparative study with established models. J. Disabil. Res. 3 (2), 20240005. https://doi.org/10.57197/JDR-2024-0005 (2024).

    Google Scholar 

  • Pratap, T., Dhulipalla, V. R. & Kokil, P. Exploring the potential of pre-trained CNN models for robust maternal–fetal ultrasound plane classification. Biomed. Signal Process. Control. 108, 107918. https://doi.org/10.1016/j.bspc.2025.107918 (2025).

    Google Scholar 

  • Guo, J., Tan, G., Wu, F., Wen, H. & Li, K. Fetal ultrasound standard plane detection with coarse-to-fine. (2022).

  • Li, F. et al. FHUSP-NET: A multi-task model for fetal heart ultrasound standard plane recognition and key anatomical structures detection. Comput. Biol. Med. 168, 107741 (2024).

    Google Scholar 

  • Oghli, M. G. et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Physica Med. 88, 127–137. https://doi.org/10.1016/j.ejmp.2021.06.015 (2021).

    Google Scholar 

  • Turkan, M., Dandil, E., Urfali, F. E. & Korkmaz, M. FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US (IEEE Access, 2025).

  • Zhao, L. et al. An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph. Future Generation Comput. Syst. 135, 234–243. https://doi.org/10.1016/j.future.2022.05.010 (2022).

    Google Scholar 

  • Lasala, A., Fiorentino, M. C., Micera, S., Bandini, A. & Moccia, S. Exploiting class activation mappings as prior to generate fetal brain ultrasound images with GANs. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1–4). IEEE. https://doi.org/10.1109/EMBC40787.2023.10340253(2023).

  • Lasala, A., Fiorentino, M. C., Bandini, A. & Moccia, S. FetalBrainAwareNet: bridging GANs with anatomical insight for fetal ultrasound brain plane synthesis. Comput. Med. Imaging Graph. 116, 102405. https://doi.org/10.1016/j.compmedimag.2024.102405 (2024).

    Google Scholar 

  • Prabakaran, B. S., Hamelmann, P., Ostrowski, E. & Shafique, M. FPUS23: an ultrasound fetus Phantom dataset with deep neural network evaluations for fetus orientations, fetal planes, and anatomical features. IEEE Access. 11, 58308–58317 (2023).

    Google Scholar 

  • Henderson, M., Shakya, S., Pradhan, S. & Cook, T. Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Mach. Intell. 2 (1), 2 (2020).

    Google Scholar 

  • Hassan, E. et al. A quantum convolutional network and ResNet-50-based classification architecture for the MNIST medical dataset. Biomed. Signal Process. Control. 87, 105560. https://doi.org/10.1016/j.bspc.2023.105560 (2024).

    Google Scholar 

  • Bilal, A. et al. BC-QNet: A quantum-infused ELM model for breast cancer diagnosis. Comput. Biol. Med. 108, 108483. https://doi.org/10.1016/j.compbiomed.2024.108483 (2024).

    Google Scholar 

  • Rao, G. E., Rajitha, B., Srinivasu, P. N., Ijaz, M. F. & Woźniak, M. Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays. Biomed. Signal Process. Control. 88, 105567. https://doi.org/10.1016/j.bspc.2023.105567 (2024).

    Google Scholar 

  • Toledo-Cortés, S., Useche, D. H., Müller, H. & González, F. A. Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression. Comput. Biol. Med. 145, 105472. https://doi.org/10.1016/j.compbiomed.2022.105472 (2022).

    Google Scholar 

  • Gao, Z. et al. Graph-enhanced ensembles of multi-scale structure perception deep architecture for fetal ultrasound plane recognition. Eng. Appl. Artif. Intell. 136, 108885. https://doi.org/10.1016/j.engappai.2024.108885 (2024).

    Google Scholar 

  • Harikumar, A., Surendran, S. & Gargi, S. Explainable AI in deep learning based classification of fetal ultrasound image planes. Procedia Comput. Sci. 233, 1023–1033 (2024).

    Google Scholar 

  • Mandal, A. K., Sen, R., Goswami, S., Chakrabarti, A. & Chakraborty, B. A new approach for feature subset selection using quantum-inspired owl search algorithm. In 2020 10th International Conference on Information Science and Technology (ICIST) (pp. 266–273). IEEE. (2020)

  • Pu, Z., Koutti, L., Masmoudi, L. & de Oliveira, J. V. A super-resolution method based on generative adversarial networks with quantum feature enhancement: application to aerial agricultural images. Neurocomputing 577, 127346 (2024).

    Google Scholar 

  • Abdulhussien, A. A., Nasrudin, M. F., Darwish, S. M. & Alyasseri, Z. A. A. Feature selection method based on quantum inspired genetic algorithm for Arabic signature verification. J. King Saud Univ. – Comput. Inform. Sci. 35 (3), 141–156. https://doi.org/10.1016/j.jksuci.2021.08.005 (2023).

    Google Scholar 

  • Li, M., Zhang, H., Fan, L. & Han, Z. A quantum feature selection method for network intrusion detection. In 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS) (pp. 281–289). IEEE. (2022)

  • Turati, G., Dacrema, M. F. & Cremonesi, P. Feature selection for classification with QAOA. In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (pp. 782–785). IEEE. (2022)

  • Chikhaoui, B. Enhancing Classification Accuracy with Quantum Non-Negative Matrix Factorization and Quantum Support Vector Machines. In 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC) (pp. 539–543). IEEE. (2025)

  • Chen, K. C., Matsuyama, H. & Huang, W. H. Learning to learn with quantum optimization via quantum neural networks. arXiv preprint arXiv:2505.00561. https://arxiv.org/abs/2505.00561(2025).

  • Akhavan, M. & Hasheminejad, S. M. H. A graph-based feature selection using class-feature association map (CFAM). In 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE) (pp. 19–24). IEEE. (2021)

  • Hatami, M., Mahmood, S. R. & Moradi, P. A graph-based multi-label feature selection using ant colony optimization. In 2020 10th International Symposium on Telecommunications (IST) (pp. 175–180). IEEE. (2020)

  • Akhiat, Y., Asnaoui, Y., Chahhou, M. & Zinedine, A. June). A new graph feature selection approach. In 2020 6th IEEE Congress on Information Science and Technology (CiSt) (156–161). IEEE. (2021).

  • Dalvand, A., Dowlatshahi, M. B. & Hashemi, A. SGFS: A semi-supervised graph-based feature selection algorithm based on the PageRank algorithm. In 2022 27th International Computer Conference, Computer Society of Iran (CSICC) (pp. 1–6). IEEE. (2022)

  • Cheng, F. et al. Graph-based feature selection in classification: structure and node dynamic mechanisms. IEEE Trans. Emerg. Top. Comput. Intell. 7 (4), 1314–1328 (2022).

    Google Scholar 

  • Zhong, J., Shang, R., Xu, S. & Li, Y. Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization. Pattern Recogn. 152, 110453. https://doi.org/10.1016/j.patcog.2024.110453 (2024).

    Google Scholar 

  • Jiang, L., Zhang, C. & Chen, F. QSeer: A Quantum-Inspired Graph Neural Network for Parameter Initialization in Quantum Approximate Optimization Algorithm Circuits. arXiv preprint arXiv:2505.06810. https://arxiv.org/abs/2505.06810(2025).

  • Li, Y. et al. Implementing graph-theoretic feature selection by quantum approximate optimization algorithm. IEEE Trans. Neural Networks Learn. Syst. 35 (2), 2364–2377 (2022).

    Google Scholar 

  • Turaka, P. & Panigrahy, S. K. Chaotic Adaptive Particle Swarm Optimization and Quantum-Inspired Genetic Algorithm for Robust Feature Selection in IoT Intrusion Detection. In 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM) (pp. 1–6). IEEE. (2025)

  • Shahriyar, M. F., Tanbhir, G., Chy, A. M. R., Tanzin, M. A. A. A. & Mashrafi, M. J. PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier. In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) (pp. 1226–1231). IEEE. (2025)

  • Yin, T. et al. A robust multilabel feature selection approach based on graph structure considering fuzzy dependency and feature interaction. IEEE Trans. Fuzzy Syst. 31 (12), 4516–4528 (2023).

    Google Scholar 

  • Nath, R. K., Thapliyal, H. & Humble, T. S. Quantum annealing for automated feature selection in stress detection. In 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) (pp. 453–457). IEEE. (2021)

  • Ye, Z., Yu, K., Guo, G. D. & Lin, S. Quantum self-organizing feature mapping neural network algorithm based on Grover search algorithm. Phys. A: Stat. Mech. Its Appl. 639, 129690. https://doi.org/10.1016/j.physa.2024.129690 (2024).

    Google Scholar 

  • He, Z. et al. Gradient-based optimization for quantum architecture search. Neural Netw. 179, 106508. https://doi.org/10.1016/j.neunet.2024.106508 (2024).

    Google Scholar 

  • Lu, S. Y., Zhang, Y. D. & Yao, Y. D. A regularized transformer with adaptive token fusion for alzheimer’s disease diagnosis in brain magnetic resonance images. Eng. Appl. Artif. Intell. 155, 111058. https://doi.org/10.1016/j.engappai.2025.111058 (2025).

    Google Scholar 

  • Lu, S. Y., Zhu, Z., Zhang, Y. D. & Yao, Y. D. Tuberculosis and pneumonia diagnosis in chest X-rays by large adaptive filter and aligning normalized network with report-guided multi-level alignment. Eng. Appl. Artif. Intell. 158, 111575. https://doi.org/10.1016/j.engappai.2025.111575 (2025).

    Google Scholar 

  • Lu, S. Y., Zhu, Z., Tang, Y., Zhang, X. & Liu, X. CTBViT: A novel ViT for tuberculosis classification with efficient block and randomized classifier. Biomed. Signal Process. Control https://doi.org/10.1016/j.bspc.2024.106981 (2025).

    Google Scholar 

  • Hekal, A. A., Elnakib, A., Moustafa, H. E. D. & Amer, H. M. Breast cancer segmentation from ultrasound images using deep dual-decoder technology with attention network. IEEE Access. 12, 10087–10101 (2024).

    Google Scholar 

  • Hekal, A. A., Amer, H. M., Elnakib, A. & https://doi.org/10.1016/j.bspc.2024.107434H. E. D., & Automatic measurement of head circumference in fetal ultrasound images using a squeeze atrous pooling UNet. Biomed. Signal Process. Control. 103, 107434 (2025).

    Google Scholar 

  • Stoean, C. et al. An assessment of the usefulness of image pre-processing for the classification of first trimester fetal heart ultrasound using convolutional neural networks. In 2021 25th International Conference on System Theory, Control and Computing (ICSTCC) (pp. 242–248). IEEE. (2021)

  • Yasrab, R. et al. A machine learning method for automated description and workflow analysis of first trimester ultrasound scans. IEEE Trans. Med. Imaging. 42 (5), 1301–1313 (2022).

    Google Scholar 

  • Li, J., Gao, Z., Wang, C., Pu, B. & Li, K. A rule-guided interpretable lightweight framework for fetal standard ultrasound plane capture and biometric measurement. Neurocomputing 621, 129290. https://doi.org/10.1016/j.neucom.2024.129290 (2025).

    Google Scholar 

  • Li, Y. et al. FNBUI-NET: A multi-task model for fetal nasal bone ultrasound image defect detection and classification. Biomed. Signal Process. Control. 104, 107586 (2025).

    Google Scholar 

  • Migliorelli, G. et al. On the use of contrastive learning for standard-plane classification in fetal ultrasound imaging. Comput. Biol. Med. 174, 108430. https://doi.org/10.1016/j.compbiomed.2024.108430 (2024).

    Google Scholar 

  • Torres, H. R. et al. A review of image processing methods for fetal head and brain analysis in ultrasound images. Comput. Methods Programs Biomed. 215, 106629. https://doi.org/10.1016/j.cmpb.2022.106629 (2022).

    Google Scholar 

  • Fiorentino, M. C., Villani, F. P., Di Cosmo, M., Frontoni, E. & Moccia, S. A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image. Anal. 83, 102629 (2023).

    Google Scholar 

  • Burgos-Artizzu, X. P. et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the Estimation of gestational age. Am. J. Obstet. Gynecol. MFM. 3 (6), 100462. https://doi.org/10.1016/j.ajogmf.2021.100462 (2021).

    Google Scholar 

  • Płotka, S. et al. BabyNet++: fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery. Comput. Biol. Med. 167, 107602. https://doi.org/10.1016/j.compbiomed.2023.107602 (2023).

    Google Scholar 

  • Belciug, S. & Iliescu, D. G. Deep learning and Gaussian mixture modelling clustering mix: A new approach for fetal morphology view plane differentiation. J. Biomed. Inform. 143, 104402. https://doi.org/10.1016/j.jbi.2023.104402 (2023).

    Google Scholar 

  • Płotka, S. S. et al. Deep learning for Estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound. Am. J. Obstet. Gynecol. MFM. 5 (12), 101182. https://doi.org/10.1016/j.ajogmf.2023.101182 (2023).

    Google Scholar 

  • Dan, T. et al. DeepGA for automatically estimating fetal gestational age through ultrasound imaging. Artif. Intell. Med. 135, 102453 (2023).

    Google Scholar 

  • Ghabri, H., Fathallah, W., Sakli, H. & Abdelkarim, M. N. Enhancing maternofetal ultrasound images toward boosting classification performance on a diverse and comprehensive data. In 2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA) (pp. 1–6). IEEE. (2023)

  • Alasmawi, H., Bricker, L. & Yaqub, M. FUSC: fetal ultrasound semantic clustering of second-trimester scans using deep self-supervised learning. Ultrasound. Med. Biol. 50 (5), 703–711. https://doi.org/10.1016/j.ultrasmedbio.2024.01.010 (2024).

    Google Scholar 

  • Dawood, Y. et al. November). Imaging fetal anatomy. Semin. Cell Dev. Biol. 131, 78–92. https://doi.org/10.1016/j.semcdb.2022.02.023 (2022).

    Google Scholar 

  • Alzubaidi, M. et al. Large-scale annotation dataset for fetal head biometry in ultrasound images. Data Brief. 51, 109708. https://doi.org/10.1016/j.dib.2023.109708 (2023).

    Google Scholar 

  • Zhao, H. et al. Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound. Med. Image. Anal. 90, 102977. https://doi.org/10.1016/j.media.2023.102977 (2023).

    Google Scholar 

  • Cai, Y. et al. Spatio-temporal visual attention modelling of standard biometry plane-finding navigation. Med. Image. Anal. 65, 101762. https://doi.org/10.1016/j.media.2020.101762 (2020).

    Google Scholar 

  • Pitchal, P., Ponnusamy, S. & Soundararajan, V. Heart disease prediction: improved quantum convolutional neural network and enhanced features. Expert Syst. Appl. 249, 123534 (2024).

    Google Scholar 

  • Saranya, R. & Jaichandran, R. Enhancing COVID-19 diagnosis from lung CT scans using optimized quantum-inspired complex convolutional neural network with ResNeXt-50. Biomed. Signal Process. Control. 95, 106295. https://doi.org/10.1016/j.bspc.2024.106295 (2024).

    Google Scholar 

  • S., Priyadharshni V., Ravi (2025) Hybrid Quantum-Convolutional Neural Network With Spatial Attention for Accurate Classification of Maternal-Fetal Planes in Ultrasound Images IEEE Access 13188306-188325 10.1109/ACCESS.2025.3625205

    Google Scholar 

Continue Reading

  • Prognostic value of CD28⁻CD57⁺CD8⁺ T cells for early immunotherapy response in hepatocellular carcinoma: a prospective observational study

  • Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68 (6), 394–424 (2018).

    Google Scholar 

  • Lee, D. H., Kim, D., Park, Y. H., Yoon, J. & Kim, J. S. Long-term surgical outcomes in patients with hepatocellular carcinoma undergoing laparoscopic vs. open liver resection: A retrospective and propensity score-matched study. Asian J. Surg. 44 (1), 206–212 (2021).

    Google Scholar 

  • Brahmer, J. R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl. J. Med. 366 (26), 2455–2465 (2012).

    Google Scholar 

  • Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N Engl. J. Med. 382 (20), 1894–1905 (2020).

    Google Scholar 

  • Budhu, A. et al. Tumor biology and immune infiltration define primary liver cancer subsets linked to overall survival after immunotherapy. Cell. Rep. Med. 4 (6), 101052 (2023).

    Google Scholar 

  • Xu, X. et al. High proportion of circulating CD8+ CD28 senescent T cells is an independent predictor of distant metastasis in nasopharyngeal canrcinoma after radiotherapy. J. Transl Med. 21 (1), 64 (2023).

    Google Scholar 

  • Reschke, R. et al. Distinct Immune Signatures Indicative of Treatment Response and Immune-Related Adverse Events in Melanoma Patients under Immune Checkpoint Inhibitor Therapy. Int J Mol Sci. ;22(15). (2021).

  • Ferrara, R. et al. Circulating T-cell Immunosenescence in patients with advanced Non-small cell lung cancer treated with Single-agent PD-1/PD-L1 inhibitors or Platinum-based chemotherapy. Clin. Cancer Res. 27 (2), 492–503 (2021).

    Google Scholar 

  • Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. European organization for research and treatment of cancer, National cancer Institute of the united States, National cancer Institute of Canada. J. Natl. Cancer Inst. 92 (3), 205–216 (2000).

    Google Scholar 

  • Llovet, J. M., Brú, C. & Bruix, J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis. 19 (3), 329–338 (1999).

    Google Scholar 

  • Zheng, Y., Wang, S., Cai, J., Ke, A. & Fan, J. The progress of immune checkpoint therapy in primary liver cancer. Biochim. Biophys. Acta Rev. Cancer. 1876 (2), 188638 (2021).

    Google Scholar 

  • Huang, A., Yang, X. R., Chung, W. Y., Dennison, A. R. & Zhou, J. Targeted therapy for hepatocellular carcinoma. Signal. Transduct. Target. Ther. 5 (1), 146 (2020).

    Google Scholar 

  • Conche, C. et al. Combining ferroptosis induction with MDSC Blockade renders primary tumours and metastases in liver sensitive to immune checkpoint Blockade. Gut 72 (9), 1774–1782 (2023).

    Google Scholar 

  • Lyu, N., Yi, J. Z. & Zhao, M. Immunotherapy in older patients with hepatocellular carcinoma. Eur. J. Cancer. 162, 76–98 (2022).

    Google Scholar 

  • Özkan, A. et al. Geriatric predictors of response and adverse events in older patients with cancer treated with immune checkpoint inhibitors: A systematic review. Crit. Rev. Oncol. Hematol. 194, 104259 (2024).

    Google Scholar 

  • Zhang, J. et al. LAMA4+ CD90+ eCAFs provide immunosuppressive microenvironment for liver cancer through induction of CD8+ T cell senescence. Cell. Commun. Signal. 23 (1), 203 (2025).

    Google Scholar 

  • Naigeon, M. et al. Human Virome profiling identified CMV as the major viral driver of a high accumulation of senescent CD8+ T cells in patients with advanced NSCLC. Sci. Adv. 9 (45), eadh0708 (2023).

    Google Scholar 

  • Ramello, M. C. et al. Polyfunctional KLRG-1+CD57+ senescent CD4+ T cells infiltrate tumors and are expanded in peripheral blood from breast cancer patients. Front. Immunol. 12, 713132 (2021).

    Google Scholar 

  • Zhang, L., Chen, X., Zu, S. & Lu, Y. Characteristics of Circulating adaptive immune cells in patients with colorectal cancer. Sci. Rep. 12 (1), 18166 (2022).

    Google Scholar 

  • Wistuba-Hamprecht, K. et al. Peripheral CD8 effector-memory type 1 T-cells correlate with outcome in ipilimumab-treated stage IV melanoma patients. Eur. J. Cancer. 73, 61–70 (2017).

    Google Scholar 

  • Giunco, S. et al. Immune senescence and immune activation in elderly colorectal cancer patients. Aging (Albany NY). 11 (11), 3864–3875 (2019).

    Google Scholar 

  • Pei, S. et al. Age-related decline in CD8+ tissue resident memory T cells compromises antitumor immunity. Nat. Aging. 4 (12), 1828–1844 (2024).

    Google Scholar 

  • Chowdhury, R. R. et al. Human coronary plaque T cells are clonal and Cross-React to virus and self. Circ. Res. 130 (10), 1510–1530 (2022).

    Google Scholar 

  • Tong, Z. et al. Single-Cell Multi-Omics identifies specialized cytotoxic and migratory CD8+ effector T cells in acute myocarditis. Circulation 152 (14), 1003–1022 (2025).

    Google Scholar 

  • Carrasco, E. et al. The role of T cells in age-related diseases. Nat. Rev. Immunol. 22 (2), 97–111 (2022).

    Google Scholar 

  • Song, M. et al. Low-Dose IFNγ induces tumor cell stemness in tumor microenvironment of Non-Small cell lung cancer. Cancer Res. 79 (14), 3737–3748 (2019).

    Google Scholar 

  • Yarchoan, M., Hopkins, A. & Jaffee, E. M. Tumor mutational burden and response rate to PD-1 Inhibition. N Engl. J. Med. 377 (25), 2500–2501 (2017).

    Google Scholar 

  • Ang, C. et al. Prevalence of established and emerging biomarkers of immune checkpoint inhibitor response in advanced hepatocellular carcinoma. Oncotarget 10 (40), 4018–4025 (2019).

    Google Scholar 

  • Xu, J. et al. Anti-PD-1 antibody SHR-1210 combined with apatinib for advanced hepatocellular Carcinoma, Gastric, or esophagogastric junction cancer: an Open-label, dose escalation and expansion study. Clin. Cancer Res. 25 (2), 515–523 (2019).

    Google Scholar 

  • Shrestha, R. et al. Monitoring immune checkpoint regulators as predictive biomarkers in hepatocellular carcinoma. Front. Oncol. 8, 269 (2018).

    Google Scholar 

  • Zhu, A. X. et al. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with Sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 19 (7), 940–952 (2018).

    Google Scholar 

  • El-Khoueiry, A. B. et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389 (10088), 2492–2502 (2017).

    Google Scholar 

  • Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 Blockade. Science 357 (6349), 409–413 (2017).

    Google Scholar 

  • Llovet, J. M., Montal, R., Sia, D. & Finn, R. S. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 15 (10), 599–616 (2018).

    Google Scholar 

  • Xu, X. et al. Clinicopathologic and prognostic significance of tumor-infiltrating CD8+ T cells in patients with hepatocellular carcinoma: A meta-analysis. Med. (Baltim). 98 (2), e13923 (2019).

    Google Scholar 

  • Zheng, Y. et al. Gut Microbiome affects the response to anti-PD-1 immunotherapy in patients with hepatocellular carcinoma. J. Immunother Cancer. 7 (1), 193 (2019).

    Google Scholar 

  • Shao, Y. Y. et al. Early alpha-foetoprotein response associated with treatment efficacy of immune checkpoint inhibitors for advanced hepatocellular carcinoma. Liver Int. 39 (11), 2184–2189 (2019).

    Google Scholar 

  • Continue Reading

  • FOCUS: Concerns Grow over Japan’s Massive Fiscal Spending under Takaichi

    FOCUS: Concerns Grow over Japan’s Massive Fiscal Spending under Takaichi

    Society

    Tokyo, Nov. 22 (Jiji Press)–A large-scale economic package adopted by the government of Japanese Prime Minister Sanae Takaichi on Friday has sparked worries about massive fiscal spending.

    The package, worth 21.3 trillion yen in terms of government spending, is the first under Takaichi, who took office a month ago.

    General-account spending under the government’s planned fiscal 2025 supplementary budget to finance measures in the package is expected to total roughly 17.7 trillion yen, up sharply from 13.9 trillion yen under the fiscal 2024 extra budget and the largest since the end of the COVID-19 pandemic.

    The new Japanese leader, who is eager to leverage fiscal spending to achieve high economic growth under the banner of “responsible and proactive” public finances, does not rule out the possibility of increasing the issuance of Japanese government bonds.

    With the Japanese government continuing to compile large-scale supplementary budgets even after the end of the pandemic, however, financial markets’ confidence in the country’s public finances and its currency is apparently starting to wane.

    [Copyright The Jiji Press, Ltd.]

    Jiji Press

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  • FOCUS: Concerns Grow over Japan’s Massive Fiscal Spending under Takaichi

    FOCUS: Concerns Grow over Japan’s Massive Fiscal Spending under Takaichi

    Society

    Tokyo, Nov. 22 (Jiji Press)–A large-scale economic package adopted by the government of Japanese Prime Minister Sanae Takaichi on Friday has sparked worries about massive fiscal spending.

    The package, worth 21.3 trillion yen in terms of government spending, is the first under Takaichi, who took office a month ago.

    General-account spending under the government’s planned fiscal 2025 supplementary budget to finance measures in the package is expected to total roughly 17.7 trillion yen, up sharply from 13.9 trillion yen under the fiscal 2024 extra budget and the largest since the end of the COVID-19 pandemic.

    The new Japanese leader, who is eager to leverage fiscal spending to achieve high economic growth under the banner of “responsible and proactive” public finances, does not rule out the possibility of increasing the issuance of Japanese government bonds.

    With the Japanese government continuing to compile large-scale supplementary budgets even after the end of the pandemic, however, financial markets’ confidence in the country’s public finances and its currency is apparently starting to wane.

    [Copyright The Jiji Press, Ltd.]

    Jiji Press

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  • Fiery UPS plane crash could spell the end for MD-11 fleet if the repairs prove too costly

    Fiery UPS plane crash could spell the end for MD-11 fleet if the repairs prove too costly

    The fiery crash of a UPS plane shortly after its left engine flew off its wing and sparked a massive fire during takeoff could spell the end of the 109 remaining MD-11 airliners that have been exclusively hauling cargo for more than a decade.

    The fate of the planes won’t be determined until after UPS, FedEx and Western Global see how expensive the repairs the Federal Aviation Administration orders will be and learn whether there is a fatal flaw in their design. The package delivery companies may have already been thinking about retiring their MD-11s — which average more than 30 years old — over the next few years and replacing them with newer planes that are safer and more efficient. The FAA grounded all MD-11s and the 10 remaining related DC-10s after the crash.

    Fourteen people — including the plane’s crew of three — died after the aircraft crashed into several businesses just outside the Muhammad Ali International Airport in Louisville, Kentucky, on Nov. 4. The plane got only 30 feet (9 meters) into the air.

    Mary Schiavo, a former U.S. Department of Transportation Inspector General, said it probably won’t be worth fixing the planes when better options are available from Boeing and Airbus, though the manufacturers have such a backlog that it takes years to get a plane after it is ordered. Still, it will depend on exactly what investigators find.

    “For them to order inspections and to ground them as readily as they did makes me think that they’re worried about them,” Schiavo said.

    The National Transportation Safety Board said Thursday that its investigators discovered cracks in key parts that failed to keep the rear of the engine attached to the UPS plane’s wing. The crash reminded experts of the 1979 disaster that killed 273 after the left engine of an American Airlines jet catapulted up and over its wing after takeoff in Chicago.

    That crash led to the worldwide grounding of 274 DC-10s, the predecessor to the MD-11. The airline workhorse was allowed to return to the skies because the NTSB determined that maintenance workers improperly using a forklift to reattach the engine damaged the plane that crashed. That meant the crash wasn’t caused by a fatal design flaw even though there had already been a number of accidents involving DC-10s.

    The lugs that the NTSB said were cracked and failed in the crash earlier this month are located close to the part that failed in the 1979 crash, but they are different. Investigators will have to determine whether there is a common defect between the UPS plane and other MD-11s or whether the problem that caused the engine to fall off was unique to the plane.

    An FAA spokesperson said the agency is working with NTSB and Boeing, which bought the company that made the MD-11s in 1997, to determine what needs to be done.

    Both the DC-10 and MD-11 have some of the highest accident rates of any commercial planes, according to statistics published annually by Boeing. Twice in the 1970s, a DC-10 lost its rear cargo door in flight. The second time in 1974 caused a crash outside Paris that killed 346 people. But airlines loved the DC-10 for years, and the Air Force maintained a fleet of dozens of tankers based on the DC-10 that it flew for decades before retiring them last year.

    Formerly independent aircraft company McDonnell Douglas announced the MD-11 in 1984. The three-engine plane appeared promising with its larger capacity and longer range than the DC-10, but its performance never fully lived up to expectations, and newer planes from Boeing and Airbus eclipsed it. Schiavo said the MD-11 was “practically obsolete” when it came out compared to two-engine planes, which are cheaper to operate. Only 200 MD-11s were built between 1988 and 2000.

    Most MD-11s started out carrying passengers, but eventually airlines decided to retire the model in favor of other planes. The last MD-11 passenger flight by KLM Royal Dutch Airlines took place in 2014.

    MD-11 aircraft made up about 9% of the UPS fleet and 4% of the FedEx fleet, the companies have said. Western Global only owns 16 MD-11 planes.

    Aviation journalist Wolfgang Borgmann, who devoted one of his “Legends of Flight” books to the history of the MD-11s and DC-10, said, “I think there is still much more useful life in them.” He pointed to the B-52 bombers that are still key planes for the Air Force even though they debuted in 1955.

    “Age doesn’t matter in aviation. It’s the maintenance that counts,” said Borgmann, editor of the Aero International magazine in Germany.

    Investigators are looking at the maintenance history of the UPS plane closely. NTSB said the last time a detailed inspection was done on its engines was in 2021. A similar inspection was not done during the extended maintenance the plane underwent the month before the crash, and the plane wasn’t due for another in-depth engine inspection until after roughly 7,000 more flights. Boeing and the FAA will have to determine whether that current maintenance schedule is adequate.

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  • 17 leading school systems face CCP action for forcing parents to buy overpriced branded supplies

    17 leading school systems face CCP action for forcing parents to buy overpriced branded supplies

    17 school systems face CCP action for forcing parents to buy overpriced branded supplies


    ISLAMABAD:

    The Competition Commission of Pakistan (CCP) has served show-cause notices on 17 leading private school systems for allegedly treating 26 million students as “captive consumers” under a tie-in arrangement by forcing them to buy up to 280% more expensive logo-bearing stationery and uniforms.

    The 17 schools are found to be engaged in tie-in practices by way of mandatory use of logo-bearing notebooks, workbooks and school uniform, according to an inquiry report released by the CCP on Friday.

    The CCP issued the show-cause notices to schools for allegedly abusing their dominant position by forcing parents to purchase expensive, logo-branded notebooks, workbooks and uniforms exclusively from school-authorised vendors, said a statement issued by the commission. The action has been taken to safeguard millions of school-going children and their families from unfair pricing practices, it added.

    The report revealed that these schools, having a total of 25.5 million enrollments that comprise 47% of total students in Pakistan, were selling stationery at prices higher by 53% to 280% than market prices.

    “The school systems under scrutiny include Beaconhouse School, the City School, Headstart, Lahore Grammar School, Froebel’s, Roots International, Roots Millennium, KIPS, Allied Schools, SuperNova, Dar-e-Arqam, STEP School, Westminster International, United Charter School and The Smart School, among others,” it said.

    These school networks operate hundreds of campuses nationwide and educate millions of students, giving them considerable influence over enrolled families, said the antitrust watchdog. The buyer is forced to purchase the tied product.

    The inquiry report stated that the schools have adopted the practice of printing their logo-bearing school supplies and appointed vendors and distributors. The exclusive vendors and distributors indicate that each school has been engaged in the production, distribution and supply of tied products in the relevant market.

    “Each school has designed its policies in a way that students are compelled to use the tied products,” said the inquiry.

    The CCP said that “there were eight school systems where the difference in quoted prices and prices of notebooks offered by these schools was more than 50%, which increased to 280% in case of some schools”.

    It compared retail prices with general, off-the-shelf notebooks to analyse the additional cost and margins in the supply chain. The analysis revealed price differences ranging from over 50% to 150%.

    The inquiry revealed that parents were mandated to buy logo-bearing notebooks, workbooks, uniforms and other ancillary products from school-authorised outlets. In several instances, schools sold compulsory “study packs” through online portals or designated vendors, with students prohibited from using generic notebooks or uniforms from the open market.

    The report concluded that leading school systems were engaged in tying arrangements, making continued enrollment conditional upon purchasing secondary products such as notebooks and uniforms. Schools appointed exclusive vendors, foreclosing the market for thousands of small stationery and uniform sellers nationwide.

    High switching costs, such as limited school options, substantial transfer fees and transportation constraints left parents with no viable alternative, enabling schools to enforce these practices without resistance. The CCP observed that these practices restricted market access, harmed small retailers and limited consumer choice.

    The CCP has directed the 17 school systems to submit written responses to show-cause notices within 14 days, appear before the commission through duly authorised representatives and explain why penalties should not be imposed.

    The CCP said that under the law, it can impose a penalty of up to 10% of the annual turnover or Rs750 million, whichever is higher, for such violations.

    Commenting on the overall education-sector status, the report underlined that between 2022-23 and 2023-24, student enrolment increased from 56 million to 58.3 million. However, in contrast to this upward trend, the total number of educational institutions declined from 349,909 to 342,547, marking a 2.1% reduction, primarily due to a decrease in private institutions.

    An estimated 25.1 million children between the ages of 5 and 16 are currently not attending school nationwide.

    At the provincial level, Punjab has the highest number of out-of-school children at 9.7 million, or 27% of the total provincial 5-16-year-old population, followed by Sindh with 7.4 million, or 44% of the total provincial 5-16 population. Khyber-Pakhtunkhwa has 4.5 million out-of-school children, or 34% of the provincial 5-16 population and Balochistan has 3.5 million children out of school, or 69% of the total.

    The report stated that as of 2023-24, private schools served 46.5% of Pakistan’s 55 million students, with a significant presence in both urban and rural areas.

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  • Exclusive | Bill Ackman Eyes Simultaneous Public Offerings of Firm and New Fund – The Wall Street Journal

    1. Exclusive | Bill Ackman Eyes Simultaneous Public Offerings of Firm and New Fund  The Wall Street Journal
    2. Bill Ackman plots IPO of hedge fund Pershing Square in early 2026  Financial Times
    3. Bill Ackman Wants To Take His Hedge-Fund Management Company, Pershing Square, Public At The Same Time As A New Closed-End Fund Next Year – WSJ  TradingView
    4. Bill Ackman eyes IPO of hedge fund Pershing in early 2026, FT reports  104.1 WIKY
    5. Bill Ackman’s Pershing Square reportedly planning IPO in early 2026  MSN

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  • World's biggest nuclear plant edges closer to restart – Japan Today

    1. World’s biggest nuclear plant edges closer to restart  Japan Today
    2. Japan edges closer to restarting world’s biggest nuclear power plant Kashiwazaki-Kariwa  BBC
    3. TEPCO set for March nuclear restart, first since Fukushima disaster  Nikkei Asia
    4. Tokyo Electric (TKECF) Secures Key Approval for Nuclear Plant Re  GuruFocus
    5. Niigata governor consents to restart of Kashiwazaki-Kariwa reactors  World Nuclear News

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  • A Fresh Look at SiTime (SITM) Valuation Following Recent Share Price Uptick

    A Fresh Look at SiTime (SITM) Valuation Following Recent Share Price Uptick

    SiTime (SITM) shares have moved slightly higher over the last day, adding almost 6% despite no major news event driving the uptick. The recent trading performance comes after a steady month and strong returns this year.

    See our latest analysis for SiTime.

    SiTime’s 1-day share price return of nearly 6% adds to an already impressive year, with its total shareholder return at 26.9% over the past twelve months and a staggering 203.5% for investors holding since 2019. Although the pace has fluctuated in recent weeks, momentum for the stock remains strong and is attracting attention as optimism around its growth story builds.

    If you’re searching for your next standout idea, now is a great opportunity to broaden your watchlist and discover fast growing stocks with high insider ownership

    With impressive returns and a strong growth trajectory, the vital question now is whether SiTime’s current valuation leaves room for upside, or if the market has already accounted for all its future potential.

    SiTime’s most widely followed narrative places its fair value well above the latest close, suggesting significant untapped upside. This perspective is built on rising expectations for product innovation and robust revenue acceleration.

    Expansion of SiTime’s content per device, particularly through customized clocks and clocking systems for AI, networking, and hyperscale platforms, enables increased dollar content per design win. This directly supports top-line growth and improves gross margins as these higher-ASP products become a greater share of sales.

    Read the complete narrative.

    Curious what’s fueling this bullish stance? The narrative hinges on aggressive assumptions around future sales expansion, margin inflection, and a valuation multiple you don’t usually see outside hyper-growth tech. One tweak to the forecasts and the whole story could shift. Don’t miss the pro-level modeling that underpins this price target.

    Result: Fair Value of $346 (UNDERVALUED)

    Have a read of the narrative in full and understand what’s behind the forecasts.

    However, risks remain, such as SiTime’s reliance on rapidly evolving AI data center demand as well as potential disruptions from innovation cycles or shifting customer dynamics.

    Find out about the key risks to this SiTime narrative.

    Looking at valuation from a price-to-sales perspective, SiTime trades at 24.8 times sales. That is far higher than both the US Semiconductor industry average of 4.2x and the peer average of 7.7x. The fair ratio is estimated at 12.5x, highlighting a substantial premium.

    What does this premium mean for risk and future upside? Could the market be overestimating SiTime’s growth story, or is innovation strong enough to justify this stretch valuation?

    See what the numbers say about this price — find out in our valuation breakdown.

    NasdaqGM:SITM PS Ratio as at Nov 2025

    If you see the story differently or want to dive deeper into the numbers, you can build your own take on SiTime in just a few minutes with Do it your way.

    A great starting point for your SiTime research is our analysis highlighting 2 key rewards and 2 important warning signs that could impact your investment decision.

    Don’t let the best opportunities pass you by. Use the Simply Wall Street Screener now to spot market movers and discover new stocks worth your attention.

    This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

    Companies discussed in this article include SITM.

    Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com

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  • A dual-domain seasonal hybrid forecasting strategy for PV power considering dynamic uncertain fluctuations

  • Liu, Z. L. et al. Prediction of long-term photovoltaic power generation in the context of climate change. Renew. Energy. 235, 13 (2024).

    Google Scholar 

  • Ruan, T. Q. et al. A new optimal PV installation angle model in high-latitude cold regions based on historical weather big data. Appl. Energy. 359, 13 (2024).

    Google Scholar 

  • Jiang, M. et al. Research on time-series based and similarity search based methods for PV power prediction. Energy Convers. Manag. 308, 24 (2024).

    Google Scholar 

  • Li, Z. et al. Heterogeneous spatiotemporal graph convolution network for multi-modal wind-PV power collaborative prediction. IEEE Trans. Power Syst. 39, 5591–5608 (2024).

    Google Scholar 

  • Zhang, H. L., Shi, J. & Zhang, C. P. A hybrid ensembled double-input-fuzzy-modules based precise prediction of PV power generation. Energy Rep. 8, 1610–1621 (2022).

    Google Scholar 

  • Wang, L. N. et al. Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model. Energy 262, 18 (2023).

    Google Scholar 

  • Cui, S. H. et al. Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE. Energy 307, 15 (2024).

    Google Scholar 

  • Peng, S. M. et al. Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model. Energy 298, 16 (2024).

    Google Scholar 

  • Pierre, A. A. et al. Peak electrical energy consumption prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU approaches. Energies 16, 12 (2023).

    Google Scholar 

  • Yang, M. et al. A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder. Renew. Energy. 194, 659–673 (2022).

    Google Scholar 

  • Ma, Y. W. et al. A Two-Stage LSTM optimization method for ultrashort term PV power prediction considering major meteorological factors. IEEE Trans. Ind. Inf. 21, 228–237 (2025).

    Google Scholar 

  • Mo, F. et al. A novel multi-step ahead solar power prediction scheme by deep learning on transformer structure. Renew. Energy. 230, 11 (2024).

    Google Scholar 

  • Son, W. & Lee, Y. R. Day-ahead prediction of PV power output: A one-year case study at Changwon in South Korea. J. Electr. Eng. Technol. 20, 71–79 (2025).

    Google Scholar 

  • Souhe, F. G. Y. et al. Optimized forecasting of photovoltaic power generation using hybrid deep learning model based on GRU and SVM. Electr. Eng. 106, 7879–7898 (2024).

    Google Scholar 

  • Wang, L. S. et al. Short-term PV power prediction based on optimized VMD and LSTM. IEEE Access. 8, 165849–165862 (2020).

    Google Scholar 

  • Tovar, M., Robles, M., Rashid, F. & Power Prediction, P. V. Using CNN-LSTM hybrid neural network model. Case of study: Temixco-Morelos, Mexico. Energies. 13, 15. (2020).

  • Li, B., Wang, H. Z. & Zhang, J. H. Short-term power forecasting of photovoltaic generation based on CFOA-CNN-BiLSTM-attention. Electr. Eng. 107, 10335–10347 (2025).

    Google Scholar 

  • Liu, M. L. et al. Day-ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization. Energy Build. 329, 15 (2025).

    Google Scholar 

  • Wang, T. S. et al. A dual-layer decomposition and multi-model driven combination interval forecasting method for short-term PV power generation. Expert Syst. Appl. 288, 16 (2025).

    Google Scholar 

  • Jiang, J. J. et al. Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model. Int. J. Low-Carbon Technol. 19, 1160–1167 (2024).

    Google Scholar 

  • Chen, G. C. et al. Photovoltaic power prediction based on VMD-BRNN-TSP. Mathematics 11, 14 (2023).

    Google Scholar 

  • Ma, W. T. et al. PV power forecasting based on relevance vector machine with sparrow search algorithm considering seasonal distribution and weather type. Energies 15, 24 (2022).

    Google Scholar 

  • Dai, H. A. et al. A short-term PV power forecasting method based on weather type credibility prediction and multi-model dynamic combination. Energy Conv Manag. 326, 20 (2025).

    Google Scholar 

  • Choudhury, S. & Dash, P. K. A hybrid neural network based solar PV power classification with time series data for bend, Oregon. Int. J. Green. Energy. 21, 2753–2770 (2024).

    Google Scholar 

  • Fan, X. W. et al. Transformer-BiLSTM fusion neural network for short-term PV output prediction based on NRBO algorithm and VMD. Appl. Sci. -Basel. 14, 19 (2024).

    Google Scholar 

  • Li, R. et al. Short-term photovoltaic prediction based on CNN-GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization, IET renew. Power Gener. 18, 908–928 (2024).

    Google Scholar 

  • Wang, X. Y. et al. Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification. Energy 240, 15 (2022).

    Google Scholar 

  • Tang, H. D. et al. Short-term photovoltaic power prediction model based on feature construction and improved transformer. Energy 320, 11 (2025).

    Google Scholar 

  • Ouyang, J. et al. Seasonal distribution analysis and short-term PV power prediction method based on decomposition optimization Deep-Autoformer. Renew. Energy. 246, 15 (2025).

    Google Scholar 

  • Liu, T. et al. Sliding time-frequency synchronous average based on autocorrelation function for extracting fault feature of bearings. Adv. Eng. Inf. 62, 16 (2024).

    Google Scholar 

  • Kreutzer, L. T. et al. S-ACF: a selective estimator for the autocorrelation function of irregularly sampled time series. Mon not Roy Astron. Soc. 522, 5049–5061 (2023).

    Google Scholar 

  • Liu, L. F. et al. SSA-GAN: singular spectrum Analysis-Enhanced generative adversarial network for multispectral pansharpening. Mathematics 13, 13 (2025).

    Google Scholar 

  • Li, Y. H. et al. SSA-LHCD: A singular spectrum Analysis-Driven lightweight network with 2-D Self-Attention for hyperspectral change detection. Remote Sens. 16, 19 (2024).

    Google Scholar 

  • Gu, J. L. et al. Generalized singular spectrum analysis for the decomposition and analysis of non-stationary signals. J. Frankl. Inst. -Eng Appl. Math. 361, 19 (2024).

    Google Scholar 

  • Mirza, A. F. et al. A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model. Energy 283, 13 (2023).

    Google Scholar 

  • Zhou, D. X. et al. Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and RIME optimized AM-TCN-BiLSTM. Energy 318, 18 (2025).

    Google Scholar 

  • Huang, S. T. et al. Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting. Energy 290, 16 (2024).

    Google Scholar 

  • Limouni, T. et al. Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model. Renew. Energy. 205, 1010–1024 (2023).

    Google Scholar 

  • Continue Reading