Sensing technology for greenhouse tomato production: A systematic review by Jingxin Yu, Jiang Liu, Congcong Sun, Jiaqi Wang, Jianchao Ci, Jing Jin, Ni Ren, Wengang Zheng, Xiaoming Wei

“…The review covers four key areas: (1) a comprehensive analysis of critical environmental factors influencing tomato growth, such as temperature, humidity, light intensity, and CO2 concentration; (2) an exploration of high-throughput, non-destructive sensing technologies, including chlorophyll fluorescence imaging, infrared CO2 sensing, and multispectral imaging; (3) an investigation of the algorithms based on multi-sensor data fusion and data-driven diagnostic systems for disease detection and growth forecasting; and (4) a discussion on potential research topics in the future to address the limitations of the existing methods. …”
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Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. …”
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MSUD-YOLO: A Novel Multiscale Small Object Detection Model for UAV Aerial Images by Xiaofeng Zhao, Hui Zhang, Wenwen Zhang, Junyi Ma, Chenxiao Li, Yao Ding, Zhili Zhang

“…Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M.…”
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Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration by Francesco Saverio Sorrentino, Marco Zeppieri, Carola Culiersi, Antonio Florido, Katia De Nadai, Ginevra Giovanna Adamo, Marco Pellegrini, Francesco Nasini, Chiara Vivarelli, Marco Mura, Francesco Parmeggiani

“…Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. …”
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NeuroSense: A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration by Tommaso Colafiglio, Angela Lombardi, Paolo Sorino, Elvira Brattico, Domenico Lofu, Danilo Danese, Eugenio Di Sciascio, Tommaso Di Noia, Fedelucio Narducci

“…We develop a comprehensive preprocessing pipeline and employ machine learning algorithms to translate EEG data into meaningful insights about emotional states. …”
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Real-world assessment of healthcare provided by the National Health Service: The network of regional Beaver research platforms by Federico Rea, Pietro Pugni, Dario Pescini, Luigi Palmieri, Simona Giampaoli, Flavia Carle, Giovanni Corrao

“…The use of standardized and validated algorithms enables to obtain regional estimates that, being obtained by employing regional platforms containing data extracted with standardized procedure, may be compared and possibly summarized by using common meta-analytic techniques. …”
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A systematic review of data privacy in Mobility as a Service (MaaS) by Zineb Garroussi, Antoine Legrain, Sébastien Gambs, Vincent Gautrais, Brunilde Sansò

“…Using the PRISMA framework, a comprehensive literature search across Web of Science, Elsevier, and IEEE Xplore databases resulted in the selection of 32 studies for detailed analysis.The review is structured around three main themes: (1) Privacy-Preserving Techniques, including anonymization strategies (k-anonymity, differential privacy, obfuscation), encryption methods (blockchain, cryptographic protocols), federated learning for decentralized data processing, and advanced algorithms for optimizing privacy budgets and balancing utility-privacy trade-offs; (2) User Trust and Privacy Perceptions, highlighting that trust in service providers is essential for MaaS adoption, privacy concerns may impact adoption but do not necessarily prevent it (the “privacy paradox”), and awareness of data misuse affects user trust and willingness to adopt MaaS; and (3) Regulatory Frameworks, focusing on the importance of GDPR compliance to ensure strict data protection through consent and transparency, and embedding privacy-by-design principles within MaaS architectures to safeguard user data from the outset.This review emphasizes the need for a holistic approach, integrating technological innovation, user-centered design, and strong regulatory oversight to effectively address privacy challenges in MaaS. …”
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Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach by Sakhr Alshwayyat, Mahmoud Bashar Abu Al Hawa, Mustafa Alshwayyat, Tala Abdulsalam Alshwayyat, Siya sawan, Ghaith Heilat, Hanan M. Hammouri, Sara Mheid, Batool Al Shweiat, Hamdah Hanifa

“…To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. …”
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Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis by Chengyuan Xu, Chengyuan Xu, Siqi Zhang, Bin Sun, Zicheng Yu, Hailong Liu

“…However, the metabolic and prognostic regulators governing this process remain largely undefined.MethodsWe constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. Results:. Pressure on palpation was the strongest clinical predictor of ACS while pain was the weakest. …”
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Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused by <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, <i>Pantoea ananatis</i> and <i>Enter... by Meng Zhang, Shuqi Tang, Chenjie Lin, Zichao Lin, Liping Zhang, Wei Dong, Nan Zhong

“…One-dimensional convolutional neural networks (1DCNNs) were employed to construct a classification model, integrating various spectral preprocessing techniques and feature selection algorithms for comparison. To enhance model robustness and mitigate overfitting due to limited spectral samples, generative adversarial networks (GANs) were utilized to augment the dataset. …”
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Assessing Functional Connectivity Dynamics During Cognitive Tasks Involving the Dorsal Stream by Huifang E. Wang, Jorge Gonzalez-Martinez, Viktor Jirsa, Patrick Chauvel, F.-Xavier Alario, Catherine Liegeois-Chauvel

“…Our results reveal distinguishable functional connectivity patterns across various cognitive tasks using clustering algorithms. Furthermore, we were able to identify specific cognitive tasks based on their unique functional connectivity signatures, with a median of accuracy 0.91. …”
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Challenges, Difficulties, and Delayed Diagnosis of Multiple Myeloma by Tugba Zorlu, Merve Apaydin Kayer, Nazik Okumus, Turgay Ulaş, Mehmet Sinan Dal, Fevzi Altuntas

“…Educational campaigns to raise awareness of the disease, algorithms dedicated to routine care and novel technologies, including AI and big data analytics, and new biomarkers may serve this purpose, as well as genomic approaches to the premalignant MGUS stage.…”
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Prospects of Predicting the Polar Motion Based on the Results of the Second Earth Orientation Parameters Prediction Comparison Campaign by Tomasz Kur, Justyna Śliwińska‐Bronowicz, Malgorzata Wińska, Henryk Dobslaw, Jolanta Nastula, Aleksander Partyka, Santiago Belda, Christian Bizouard, Dale Boggs, Sara Bruni, Lue Chen, Mike Chin, Sujata Dhar, Robert Dill, PengShuo Duan, Jose M. Ferrandiz, Junyang Gou, Richard Gross, Sonia Guessoum, Songtao Han, Robert Heinkelmann, ChengLi Huang, Christopher Irrgang, Jacek Kudrys, Jia Li, Marcin Ligas, Lintao Liu, Weitao Lu, Volker Mayer, Wei Miao, Maciej Michalczak, Sadegh Modiri, Michiel Otten, Todd Ratcliff, Shrishail Raut, Jan Saynisch‐Wagner, Matthias Schartner, Erik Schoenemann, Harald Schuh, M. Kiani Shahvandi, Benedikt Soja, Xiaoqing Su, Daniela Thaller, Maik Thomas, Guocheng Wang, Yuanwei Wu, CanCan Xu, Xueqing Xu, Xinyu Yang, Xin Zhao, Zhijin Zhou

“…Abstract Growing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. …”
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Detecting Emerging DGA Malware in Federated Environments via Variational Autoencoder-Based Clustering and Resource-Aware Client Selection by Ma Viet Duc, Pham Minh Dang, Tran Thu Phuong, Truong Duc Truong, Vu Hai, Nguyen Huu Thanh

“…Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. …”
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2H-MoS2 lubrication-enhanced MWCNT nanocomposite for subtle bio-motion piezoresistive detection with deep learning integration by Ke-Yu Yao, Derek Ka-Hei Lai, Hyo-Jung Lim, Bryan Pak-Hei So, Andy Chi-Ho Chan, Patrick Yiu-Man Yip, Duo Wai-Chi Wong, Bingyang Dai, Xin Zhao, Siu Hong Dexter Wong, James Chung-Wai Cheung

“…Intelligent piezoresistive health monitoring systems integrate advanced nanocomposite architectures with precise algorithmic analysis for real-time physiological assessment. …”
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A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case by Jose Miguel Riquelme-Dominguez, Manuel Carranza-García, Pedro Lara-Benítez, Francisco M. González-Longatt

“…Experimental assessment has been carried out using Typhoon HIL-404 simulator, verifying that both algorithms are suitable for real-time simulation.…”
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Smart sensing of creatinine in urine samples: Leveraging Cu-nanowires/MoS2 quantum dots and machine learning by Geethukrishnan, Paresh Prakash Bagde, Sammishra KH, Chandranath Adak, Rajendra P. Shukla, Kiran Kumar Tadi

“…In order to address this issue, we implemented a machine learning (ML) approach to accurately quantify CRT levels in complex mixtures, as well as in urine samples. The ML algorithms employed are trained and tested on a large dataset, allowing them to effectively capture and analyze the variance in the electrochemical signatures, demonstrating the application of artificial intelligence. …”
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