Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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Analyzing Post-fire Vegetation Dynamics with Ultra-high Resolution Remote Sensing Data by O. Petrov, O. Petrov, A. Medvedev

“…Future research should focus on improving tree segmentation of SfM-MVS point clouds in dense canopies, optimizing co-alignment under varying environmental conditions, and integrating additional point cloud classification methods to improve accuracy in areas with complex species distribution.…”
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A Novel Active RFID and TinyML based system for livestock Localization in Pakistan by Syed Atir Raza Shirazi, Maham Fatima, Abdul Wahab, Sadaf Ali

“…To realize the full potential of this unique Active RFID and TinyML-based livestock localization system in Pakistan, further research should focus on optimizing localization algorithms, enhancing TinyML models, and exploring interaction with upcoming technologies …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…However, challenges persist, including data privacy, algorithmic bias, ethical dilemmas, and regulatory hurdles, emphasizing the need for robust governance. …”
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Benefits and Challenges of AI-Based Digital Twin Integration in the Saudi Arabian Construction Industry: A Correspondence Analysis (CA) Approach by Aljawharah A. Alnaser, Haytham Elmousalami

“…However, several challenges hinder the realization of these benefits, including a lack of standardization of integrated DT and AI in construction projects, a lack of understanding of AI’s capabilities, a lack of logistics and the limited availability of IT infrastructure, and the complexity of AI algorithms. These findings underscore the transformative potential of integrating AI-driven DTs to optimize construction performance, improve decision-making, and address real-world complexities. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…Although segmentation algorithms based on convolutional neural networks (CNNs) or Transformers have achieved significant performance improvements, the trade-off between segmentation precision and computational complexity remains a key limitation for practical applications. …”
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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence by David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios

“…AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. …”
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Advancing Grid-Forming Inverter Technology: Comprehensive PQ Capability and Performance Analysis by Md Nurunnabi, Shuhui Li, Himadry Shekhar Das

“…Recognizing that IBR operational constraints are distinct from those of synchronous generators, this study develops advanced PQ capability models and algorithmic frameworks that accurately characterize GFM inverter operational constraints across various coupling filter configurations (L, LC, and LCL). …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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Integrating cyber-physical systems with embedding technology for controlling autonomous vehicle driving by Manal Abdullah Alohali, Hamed Alqahtani, Abdulbasit Darem, Monir Abdullah, Yunyoung Nam, Mohamed Abouhawwash

“…Deep reinforcement learning (DRL) has emerged as a strong tool for dealing with such uncertainty, yet current DRL models struggle to ensure safety and optimal behaviour in indeterminate settings due to the difficulties of understanding dynamic reward systems. …”
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Advancing Alzheimer's Therapy: Computational strategies and treatment innovations by Jibon Kumar Paul, Abbeha Malik, Mahir Azmal, Tooba Gulzar, Muhammad Talal Rahim Afghan, Omar Faruk Talukder, Samar Shahzadi, Ajit Ghosh

“…Moreover, machine learning algorithms enhance early diagnosis and enable precision medicine strategies by analyzing multi-modal datasets. …”
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Decentralized Coordination of Temperature Control in Multiarea Premises by Maria Yukhymchuk, Volodymyr Dubovoi, Viacheslav Kovtun

“…The proposed coordination algorithms make it possible to optimize the operating modes of the system automatically when its structure and/or settings are changed. …”
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The role of artificial intelligence in enhancing disaster preparedness and trauma care by Mohamad Sorani

“…Furthermore, ethical considerations such as data privacy, algorithmic bias, and equitable access are crucial to ensuring that AI-driven solutions are implemented responsibly and inclusively.This review also explores future directions for AI, including the development of sophisticated predictive models, autonomous systems for high-risk tasks, immersive training programs utilizing virtual and augmented reality, and global collaboration aimed at standardizing AI tools and practices. …”
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Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. …”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…We explore more sophisticated approaches like algorithms for deep learning (DL) and machine learning (ML) in addition to more conventional approaches like analysis of time series and models of regression. …”
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Intelligent gear decision method for vehicle automatic transmission system based on data mining by Yong Wang, Jianfeng Zeng, Pengfei Du, Huachao Xu

“…Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. …”
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Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence by N. Mohammad, M. Khan, M. Maqsood, A. H. K. Naseeb

“…AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. …”
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Forecasting motion trajectories of elbow and knee joints during infant crawling based on long–short-term memory (LSTM) networks by Jieyi Mo, Qiliang Xiong, Ying Chen, Yuan Liu, Xiaoying Wu, Nong Xiao, Wensheng Hou

“…It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective control strategies to improve assistive crawling devices.…”
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