Hybrid neural network method for damage localization in structural health monitoring by Fatahlla Moreh, Yusuf Hasan, Zarghaam Haider Rizvi, Sven Tomforde, Frank Wuttke

“…Abstract The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning algorithms or traditional approaches that typically use image data. …”
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Load Probability Density Forecasting Under FDI Attacks Based on Double-Layer LSTM Quantile Regression by Pei Zhao, Jie Zhang, Guang Ling

“…According to our research, the suggested model can successfully describe future power systems full of possible risks and uncertainties with great accuracy.…”
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APPLICATON OF MICROSEISMIC NOISE ESTIMATION IN OIL AND GAS EXPLORATION AIMED AT REDUCING ENVIRONMENTAL IMPACT by A. Yu. Tsivadze, Yu. V. Sirotinskiy, M. A. Abaturov

“…This article discusses the possibility of reducing ecological costs and risks during exploration of oil and gas fields. To this end, we propose to reduce the number of exploratory drilling works performed using unjustified non‐productive wells. …”
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Evaluating the Efficacy of the More Young HIFU Device for Facial Skin Improvement: A Comparative Study with 7D Ultrasound by Ihab Adib, Youjun Liu

“…The More Young device features enhanced focal depth precision and energy delivery algorithms, including nine pre-programmed stabilization checkpoints to minimize treatment risks. …”
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Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…The system includes a combination of real-time continuous glucose monitoring from a continuous glucose monitoring device and a control algorithm to direct insulin delivery through an insulin pump. …”
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Public and mental health professionals’ perspectives on social media and suicide exposure by Jo Bell, Christopher Westoby

“…Results Four key themes emerged: (1) The communicative ecology of social media (where the public act as content purveyors, rapidly disseminating varied and often unregulated narratives); (2) Harmful effects (including the copycat effect and toxic discourse); (3) Positive effects (where protective discourse and moderation offer harm reduction opportunities); and (4) Challenges in intervention (including content moderation difficulties and algorithmic biases that amplify harmful narratives). …”
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Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning by Adrien Le Guillou, Simona Niculescu

“…Coastal wetlands protect against erosion, reduce flood risks, and maintain watercourses during periods of drought, which can mitigate global warming and its effects on humans. …”
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Strategies to Reduce Left Anterior Descending Artery and Left Ventricle Organ Doses in Radiotherapy Planning for Left-Sided Breast Cancer by Umut Diremsizoglu, Nezihan Topal, Aykut Oguz Konuk, Ibrahim Halil Suyusal, Dogacan Genc, Onur Ari, Hasan Furkan Cevik, Aysegul Ucuncu Kefeli, Maksut Gorkem Aksu, Emine Binnaz Sarper

“…Conclusions: We achieved significant reductions in all heart, LAD, and LV parameters without making any changes to the planned treatment volume coverage by adding LAD and LV OARs to the optimization algorithms. …”
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Sleep stages classification based on feature extraction from music of brain by Hamidreza Jalali, Majid Pouladian, Ali Motie Nasrabadi, Azin Movahed

“…A total of 19 features are extracted from the sequence of notes and fed into feature reduction algorithms; the selected features are applied to a two-stage classification structure: 1) the classification of 5 classes (merging S1 and REM-S2-S3-S4-W) is made with an accuracy of 89.5 % (Cap sleep database), 85.9 % (Sleep-EDF database), 86.5 % (Sleep-EDF expanded database), and 2) the classification of 2 classes (S1 vs. …”
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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

“…., 85% accurate algal bloom prediction 7 days in advance) and operational optimization (15% cost reduction in wastewater treatment). Hybrid frameworks such as sensor fusion systems (reducing measurement errors by 83%) and digital twins (preventing 60% of public health risks during contamination events) demonstrate transformative potential by bridging these approaches. …”
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A conceptual digital twin framework for supply chain recovery and resilience by Oluwagbenga Victor Ogunsoto, Jessica Olivares-Aguila, Waguih ElMaraghy

“…Amidst escalating global supply system risks and interruptions, the imperative for fortified supply networks is evident. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Extensive feature analysis was conducted to identify the most impactful clinical features followed by feature reduction techniques to focus on the most informative clinical features, reducing model complexity and overfitting risks. …”
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Optimization and image quality in CT studies: application of DRLs based on clinical indication by Carmine Picone, Annamaria Porto, Roberta Fusco, Claudio Granata, Maria Chiara Brunese, Vincenza Granata, Alessandro Ottaiano, Marialuisa Barretta, Ferdinando Caranci, Biagio Pecori, Eugenio Sorgente, Sergio Salerno, Raffaella Mormile, Fabio Pinto, Antonio Pinto, Andrea Magistrelli, Antonella Petrillo

“…Recent advancements, including iterative reconstruction algorithms and artificial intelligence, offer promising avenues for dose reduction without compromising diagnostic value. …”
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Trading off Iodine and Radiation Dose in Coronary Computed Tomography by Guillaume Fahrni, Thomas Saliba, Damien Racine, Marianna Gulizia, Georgios Tzimas, Chiara Pozzessere, David C. Rotzinger

“…This review explores the balance between radiation and CM volumes, emphasizing patient-specific protocol optimization to improve diagnostic accuracy while minimizing risks. Radiation dose reduction strategies, such as low tube voltage protocols, prospective ECG-gating, and modern reconstruction algorithms, have significantly decreased radiation exposure, with some studies achieving sub-millisievert doses. …”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…Radiation therapy (RT) is widely used to treat thoracic cancers but carries a risk of radiation-induced heart disease (RIHD). This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. …”
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Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI... by Nuray Vakitbilir, Rahul Raj, Donald E. G. Griesdale, Mypinder Sekhon, Francis Bernard, Clare Gallagher, Eric P. Thelin, Logan Froese, Kevin Y. Stein, Andreas H. Kramer, Marcel J. H. Aries, Frederick A. Zeiler

“…These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. …”
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Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions by Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Mette Sagbakken, Ahmed Ghannam, Fuad H. Abuadas, Joel Somerville, Joel Somerville, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair, Abbas Al Mutair

“…These efficiencies allowed nursing teams to devote more time to direct patient care and were associated with reductions in burnout and improved workplace morale.Nursing perceptionsAcross practice settings, nursing students and practicing nurses broadly welcomed AI’s ability to streamline workflows and support decision-making, recognizing its potential to elevate patient care and professional practice.Ethical implicationsSimultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.Framework and recommendationsThe Nursing AI Integration Roadmap (NAIIR) was developed, emphasizing transformational education, advanced clinical integration, ethical governance, robust organizational infrastructure, participatory design, and rigorous economic evaluation. …”
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AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

“…By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. …”
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Artificial intelligence in nursing: an integrative review of clinical and operational impacts by Salwa Hassanein, Salwa Hassanein, Rabie Adel El Arab, Rabie Adel El Arab, Amany Abdrbo, Mohammad S. Abu-Mahfouz, Mastoura Khames Farag Gaballah, Mohamed Mahmoud Seweid, Mohamed Mahmoud Seweid, Mohammed Almari, Husam Alzghoul

“…Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. …”
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The Emergence of AI-Driven Virtual Hospitals: Redefining Patient Care Beyond Physical Boundaries by Ifrah Hameed, Hafiz Muhammad Haseeb Khaliq

“…Despite these apparent benefits, the implementation of AI-based virtual hospitals faces challenges in terms of security such as data privacy, cybersecurity risks, and business risks due to overfitting and underfitting models. …”
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