A NARRATIVE REVIEW ON AUTOMATION AND DIGITALIZATION IN QUALITY CONTROL LABORATORIES OF PHARMACEUTICALS by Sheikh Abdul Khaliq, Muhammad Kashif, Muhammad Jiyad Shaikh, Ijaz Hussain

“…For instance, Sysmex's TLA system enhances automation within QC procedures, leading to increased accuracy and reduced errors. Moreover, technologies like IoT-based monitoring systems, robotic sample handlers, and AI-based tools—such as AI-based pathogen detection systems—are being implemented. …”
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Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation. by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This highlights the need for automated detection systems to enhance diagnostic efficiency. …”
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An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks by Abdullilah A. Alotaibi, Salman A. AlQahtani

“…This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. …”
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Investigating the Impacts of Ionospheric Irregularities on Precise Point Positioning Over China and Its Mechanism by Wei Li, Shuli Song, Weili Zhou, Na Cheng, Chao Yu

“…The results show that the ionospheric irregularities caused increased positioning errors (decimeter‐ to meter‐level), enlarged phase residuals (decimeter‐level), and increased the number of detected cycle slips in PPP processing in low latitude regions of China. …”
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Cosmological Constraints from Combining Photometric Galaxy Surveys and Gravitational Wave Observatories by E.L. Gagnon, D. Anbajagane, J. Prat, C. Chang, J. Frieman

“…Spatial variations in survey properties due to selection effects generate substantial systematic errors in large-scale structure measurements in optical galaxy surveys on very large scales. …”
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The role of spectral vs spatial resolution of satellite data on the accuracy of mapping unburned vegetation within fire scar perimeters by Magdalini Pleniou, Nikos Koutsias

“…Our research highlights several key findings including: (a) spectral information content appears to have a significant role when considering the full range of separability values, (b) in combinations featuring high separability values, the primary parameter that influences the accuracy of the mapping the unburned vegetation is the spatial resolution of the satellite images, and (c) there is a discernible disparity in the roles of the spectral and spatial resolution concerning the commission and omission errors of the unburned class.…”
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Incorporation of XAI and Deep Learning in Biomedical Imaging: A Review by Sushil K. Singh, Bal Virdee, Saurabh Aggarwal, Abhilash Maroju

“…Concerns about liability in autonomous car accidents are comparable to those associated with deep learning applications in medical imaging. Errors such as false positives and false negatives can negatively affect patients' health. …”
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Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques by Phan Thi Huong, Lam Thanh Hien, Nguyen Minh Son, Huynh Cao Tuan, Thanh Q. Nguyen

“…While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. …”
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Anisotropic flexible stress sensors based on Loofah/CN/MWCNTs by 刘璐, 贾晓丽, 黄书童, 张景龙, 李守宝, 柯燎亮

“…These findings further confirmed the excellent reliability and stability of the sensor, establishing a strong foundation for its application in electronic skins, wearable pressure detection devices, and related fields.To investigate the mechanical characteristics of the sensor's strain response, a series of dynamic compression/recovery response tests were conducted. …”
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Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…Among them, CatBoost achieved the highest predictive performance with an R2 of 0.979 and the lowest mean squared error (MSE) of 0.006 on the test set. SHAP analysis revealed that nanoparticle concentration was the most influential feature. …”
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An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction by Suranjana Mitra, Annwesha Banerjee Majumder, Tanusree Saha

“…Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. …”
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Deep Learning Autoencoders for Fast Fourier Transform-Based Clustering and Temporal Damage Evolution in Acoustic Emission Data from Composite Materials by Serafeim Moustakidis, Konstantinos Stergiou, Matthew Gee, Sanaz Roshanmanesh, Farzad Hayati, Patrik Karlsson, Mayorkinos Papaelias

“…Markov chain analysis captures the transitions between damage states, providing a predictive framework for assessing damage progression. These findings highlight the potential of the proposed approach for early damage detection and predictive maintenance, which significantly improves the effectiveness of AE-based SHM systems in reducing downtime and extending component lifespan.…”
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Global self-localization and navigation using 3D-LiDAR for headland turning in vineyards by Kanya Usu, Yoshitomo Yamasaki, Kazunobu Ishii, Noboru Noguchi

“…The results showed a 0.101 m root mean square error (RMSE) between the estimated position and the ground truth acquired through the RTK-GNSS. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…Abstract Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. …”
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A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction by Christopher Nielsen, Matthias Wilms, Nils D. Forkert

“…By leveraging foundation models and multimodal retinal imaging, the proposed approach enhances disease classification accuracy and demonstrates the potential of integrating the RAG into clinical workflows as a scalable, non-invasive screening tool. Significance: The findings underscore the potential of multimodal retinal imaging to transform RAG into a clinically relevant and highly accessible biomarker for disease detection.…”
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…Consequently, the Tikhonov model outperforms the Yandex Boosting model due to its higher accuracy and lower error rates, whereas Yandex Boosting, despite strong training performance, suffers from overfitting, leading to inferior testing performance. …”
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Cross-Line Fusion of Ground Penetrating Radar for Full-Space Localization of External Defects in Drainage Pipelines by Yuanjin Fang, Feng Yang, Xu Qiao, Maoxuan Xu, Liang Fang, Jialin Liu, Fanruo Li

“…The results demonstrate that the proposed approach achieves axial positioning errors of less than 2.0 cm, spatial angular positioning errors below 2°, and depth coordinate errors within 2.3 cm. …”
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Assessment of Nurses Knowledge Concerning Type 2 Diabetes Mellitus Management with Insulin Therapy in Intensive Care Units at Baghdad Hospitals by Sabah Abbas Ahmed, Khames Bander Abed, Ali H. Alek Al-Ganmi

“…Nevertheless, further studies are necessary in order to assess nurses' knowledge toward DM management with insulin therapy in ICUs and demonstrate the errors that occur, which are lead to fatal complications. …”
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Modelling cognitive decline in the Hypertension in the Very Elderly Trial [HYVET] and proposed risk tables for population use. by Ruth Peters, Nigel Beckett, Robert Beardmore, Rafael Peña-Miller, Kenneth Rockwood, Arnold Mitnitski, Shahrul Mt-Isa, Christopher Bulpitt

“…Cognitive states were defined in relation to errors on the Mini-Mental State Examination, with more errors signifying worse cognition. …”
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Maintenance Time Prediction for Predictive Maintenance of Ship Engines by Seunghun Lim, Jungmo Oh, Jinkyu Park

“…And through comparison and verification using machine learning, the average mean absolute error (MAE) across all cylinders was 2.916 for the RGCCV-based method and 8.138 for the temperature-based method, demonstrating a 64% improvement. …”
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