Challenges of the Biopharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review by Johnderson Nogueira de Carvalho, Felipe Rodrigues da Silva, Erick Giovani Sperandio Nascimento

“…The results obtained revealed that prescriptive maintenance offers opportunities for improvement in the production process, such as cost reduction and greater proximity to all actors in the areas of production, maintenance, quality, and management. …”
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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

“…Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…ML-driven solutions, including LSTM networks and random forest models, enable real-time anomaly detection (e.g., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). …”
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Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms by Jorge Enrique Chaparro, José Edinson Aedo, Felipe Lumbreras Ruiz

“…In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. …”
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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model. by Ayesha Khalid, Afshan Kaleem, Wajahat Qazi, Roheena Abdullah, Mehwish Iqtedar, Shagufta Naz

“…Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable tools for predicting O-GlcNAc sites, reducing experimental costs, and enhancing efficiency. …”
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Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery by Tarek Berghout, Eric Bechhoefer, Faycal Djeffal, Wei Hong Lim

“…This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.…”
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The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval by Yucheng Gao, Lixia Ma, Zhongqi Zhang, Xianzhang Pan, Ziran Yuan, Changkun Wang, Dongsheng Yu

“…These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations.…”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

“…We provide a rigorous theoretical proof of convergence, showing that PP-QADMM converges to the optimal solution for convex problems while achieving a convergence rate comparable to standard ADMM, but with significantly lower communication and energy costs, and robust privacy protection. …”
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Integrating Positron Emission Tomography Combined with Computed Tomography Imaging into Advanced Radiation Therapy Planning: Clinical Applications, Innovations, and Challenges by Subhash Chand Kheruka, Anjali Jain, M. Sharjeel Usmani, Naema Al-Maymani, Noura Al-Makhmari, Huda Al-Saidi, Sana Al-Rashdi, Anas Al-Balushi, Vipin Jayakrishnan, Khulood Al-Riyami, Rashid Al-Sukaiti, Raza Sayani

“…The review also addresses persistent barriers, including limited tracer specificity, spatial resolution constraints, integration complexity, and high implementation costs. Beyond technical discussions, we reflect on emerging ethical considerations, such as transparency in AI-driven planning, patient consent in algorithm-assisted treatment decisions, and the need for equitable access to PET/CT technologies. …”
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Impact of a massive earthquake on adherence to antiretroviral therapy, mental health, and treatment failure among people living with HIV in Nepal. by Bharat Singh Negi, Sunil Kumar Joshi, Minato Nakazawa, Tomohiro Kotaki, Anup Bastola, Masanori Kameoka

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Performance of Machine Learning Classifiers for Diabetes Prediction by Mijala Manandhar, Shaikat Baidya, Babalpreet Kaur, Katia Atoji

“…Logistic Regression and Multilayer Perceptron also showed robust results, but SGD was superior in most metrics. For the Rules classifiers, JRip outperformed others due to its iterative rule optimization, whereas OneR's simplicity resulted in the lowest performance. …”
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Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions by Alireza Alibakhshi, Amirreza Saffarian, Erfan Hassannayebi

“…It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.…”
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Resource Scheduling for Cloud Data Center Based on Data Mining in Smart Grid by Songtao Peng

“…Since the wide use of virtual technology,the resource use rate in cloud data center has been improved effectively than ever before.However,there is still a large space for improvement due to the resources usually are pre-started and pre-allocated by the user demand rather than the actual needs.In order to allocate available resource more accurately,two algorithms were proposed to meet the needs of the daily use in most of time.The available virtual resources would be arranged according the forecast using the algorithms of hierarchical composition of loading and the peek resources needs would be dynamic allocated using the algorithms of stochastic equilibrium and queuing theory.The results of experiment via the system based upon above theories show that the solution provides a kind of very effective advanced means for the optimal use of resources and energy saves.…”
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Enhancing Model Accuracy of UAV-Based Biomass Estimation by Evaluating Effects of Image Resolution and Texture Feature Extraction Strategy by Yaxiao Niu, Xiaoying Song, Liyuan Zhang, Lizhang Xu, Aichen Wang, Qingzhen Zhu

“…Maize AGB estimation models were established based on SIs only and combination of SIs and TFs using machine learning algorithms. We explored the impacts of spatial resolution and TF_CP on the performance of AGB models and analyzed the potentials of combination of SIs and TFs for improving maize AGB estimation accuracy. …”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…Based on the 7-year (2018–2024) in situ measurements from Beijing, Nanjing, and Shanghai, validation results reveal that AngleNet achieves substantial improvements, with an average R2 of 0.71 and a root mean square error (RMSE) of 10.39%, surpassing conventional models such as LGBM (light gradient boosting machine) and RF (random forest) by over 10% in both metrics, and demonstrating a remarkable 41% increase in R2 and a 10% reduction in RMSE compared to the previous BRNN method (batch normalization and robust neural network). …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Compared to existing superficial deposit maps, our peat predictions significantly improve the spatial detail of peatlands at the national level, offering new opportunities for land use planning and emission mitigation. …”
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Feasibility of Implementing Motion-Compensated Magnetic Resonance Imaging Reconstruction on Graphics Processing Units Using Compute Unified Device Architecture by Mohamed Aziz Zeroual, Natalia Dudysheva, Vincent Gras, Franck Mauconduit, Karyna Isaieva, Pierre-André Vuissoz, Freddy Odille

“…Motion correction in magnetic resonance imaging (MRI) has become increasingly complex due to the high computational demands of iterative reconstruction algorithms and the heterogeneity of emerging computing platforms. …”
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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…In this study, a VIV dataset of a cylindrical body with different degrees of freedom is used to compare the performance of the PINN and three PINN optimization algorithms. The findings suggest that, compared to a standard PINN, the AW-PINN lowers the mean squared error (MSE) on the test set by 50%, significantly improving the prediction accuracy. …”
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Consensus recommendations for diagnosis and management of pulmonary arterial hypertension patients in Egypt by Ayman Farghaly, Ahmed A. Aziz, Reem El Korashy, Marwa Abdelrady, Wael Soliman, Ahmed Hassan, Youssef Amin Soliman

“…This should be coupled with the development of screening algorithms tailored to the Egyptian setting. To develop such national screening algorithms, cost-effectiveness studies should be conducted in Egypt to better understand optimal screening frequency and the best use of algorithms. …”
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