Development and Validation of a Questionnaire for the Assessment of Diabetic Peripheral Neuropathy: Implications for Clinical Practice in Physical Therapy and Patient Management by Constantin CIUCUREL, Mariana TUDOR, Elena Ioana ICONARU

“…Introduction: The aim of this study is to develop and validate a questionnaire that evaluates the impact of diabetic peripheral neuropathy (DPN) on quality of life and functional capacity in older adults, while also exploring correlations between clinical factors and DPN severity to guide personalized treatment strategies. …”
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Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover by Rongpeng He, Jihua Meng, Yanfei Du, Zhenxin Lin, Xinyan You, Xinyu Gao

“…Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…In addition, eight different machine learning algorithms were applied for prediction, and the prediction performances of these algorithms were comprehensively evaluated using the receiver operating characteristic curve, area under the curve (AUC), calibration curve analysis, and decision curve analysis and confusion matrix.ConclusionsUsing LASSO regression analysis, leukocyte, neutrophil, red blood cell, hemoglobin, platelet, and monocyte-to-lymphocyte ratios were identified as risk factors for HF. …”
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Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices by Chao Ping Wu, Rachel Benish Shirley, Alex Milinovich, Kaiyin Liu, Eduardo Mireles-Cabodevila, Hassan Khouli, Abhijit Duggal, Anirban Bhattacharyya

“…The LightGBM model outperformed other algorithms, achieving a AUROC of 0.91 (95%CI 0.9–0.91) and performance was held in the 2020 validation cohort (n = 509) with an AUROC of 0.85 (95%CI 0.84–0.85). …”
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Optimizing image capture for low-light widefield quantitative fluorescence microscopy by Zane Peterkovic, Avinash Upadhya, Christopher Perrella, Admir Bajraktarevic, Ramses E. Bautista Gonzalez, Megan Lim, Kylie R. Dunning, Kishan Dholakia

“…The tutorial covers a detailed discussion of user-controllable parameters, as well as the application of post-processing algorithms for denoising. We illustrate these concepts using autofluorescence images of live mammalian embryos captured with a two-photon light sheet fluorescence microscope.…”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. By integrating AI techniques, such as back propagation neural networks, the framework predicts variables with high computational complexity, improving accuracy and simulation efficiency. …”
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A Study on Non-Contact Multi-Sensor Fusion Online Monitoring of Circuit Breaker Contact Resistance for Operational State Awareness by Zheng Wang, Hua Zhang, Yiyang Zhang, Haoyong Zhang, Jing Chen, Shuting Feng, Jie Guo, Yanpeng Lv

“…The contact condition of circuit breaker contacts directly affects their operational reliability, while circuit resistance, as a key performance indicator, reflects physical changes such as wear, oxidation, and ablation. …”
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Machine learning-driven nanoparticle toxicity by Zied Hosni, Sofiene Achour, Fatma Saadi, Yangfan Chen, Mohammed Al Qaraghuli

“…By using a robust dataset, we deployed Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms to identify key NP features that significantly influence cellular toxicity. …”
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Symmetric Grayscale Image Encryption Based on Quantum Operators with Dynamic Matrices by Luis Olvera-Martinez, Manuel Cedillo-Hernandez, Carlos Adolfo Diaz-Rodriguez, Leonardo Faustinos-Morales, Antonio Cedillo-Hernandez, Francisco Javier Garcia-Ugalde

“…However, existing encryption algorithms often involve complex mathematical operations or require specialized hardware, which limits their efficiency and practicality. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Optimization approaches, including machine learning-based algorithms, multi-objective optimization, and hybrid methods, have enhanced key performance indicators, such as material removal rate (MRR), surface quality, and tool wear, thereby increasing process efficiency and reducing machining time. …”
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Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review by Mahmoud Saleh Al-Khafaji, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, Dhiya Al‑Jumeily

“…The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. …”
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AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes by David B. Olawade, Navami Leena, Eghosasere Egbon, Jeniya Rai, Aysha P. E. K. Mohammed, Bankole I. Oladapo, Stergios Boussios

“…The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. …”
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Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques by Yang Li, Kun Zou, Yixuan Wang, Yucheng Zhang, Jingtao Zhong, Wu Zhou, Fang Tang, Lu Peng, Xusheng Liu, Lili Deng

“…Specifically, it attained AUC values of 0.8 and 0.765 in the training and test sets, respectively, while achieving accuracy scores of 0.736 and 0.728 in these two datasets. …”
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An intelligible AI-driven decision support system for poststroke mobility assessment by Jin Cheng Liaw, Dominik Raab, Malte Weber, Mario Siebler, Harald Hefter, Dörte Zietz, Marcus Jäger, Andrés Kecskeméthy, Francisco Geu Flores

“…To meet these challenges, we show that machine learning algorithms can reproduce expert mobility assessment from gait data with acceptable accuracy, supporting poststroke evaluation while giving intelligible feedback into how the assessments were generated. …”
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Time to Disease Control with Dupilumab for Bullous Pemphigoid: A Systematic Review and Meta-analysis by Laurence Mainville, Lily Xu, Vincent Piguet, Aaron M. Drucker, David O. Croitoru

“…Randomized controlled trials are needed to determine dupilumab’s place in therapeutic algorithms for BP. PROSPERO number: CRD42024599235 …”
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Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research by Mahbub Hassan, Md. Emtiaz Kabir, Muzammil Jusoh, Hong Ki An, Michael Negnevitsky, Chengjiang Li

“…The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. …”
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Artificial intelligence as a transforming factor in motility disorders–automatic detection of motility patterns in high-resolution anorectal manometry by Miguel Mascarenhas, Francisco Mendes, Joana Mota, Tiago Ribeiro, Pedro Cardoso, Miguel Martins, Maria João Almeida, João Rala Cordeiro, João Ferreira, Guilherme Macedo, Cecilio Santander

“…A dataset of 701 HR-ARM exams from a tertiary center, classified according to London Classification, was used to develop and test multiple machine learning (ML) algorithms. The exams were divided in a training and testing dataset with a 80/20% ratio. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

“…By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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Artificial Intelligence and Ethical Dimensions of Automated Traffic Enforcement: Implications for Public Health, Healthcare Equity, and Social Justice by Patricia Haley

“…This study provides a critical examination of AI-integrated speed and red-light camera systems through the theoretical lenses of Surveillance Capitalism, the Panopticon Model, Social Control Theory, Technological Determinism, and Structural Violence Theory. While artificial intelligent speed safety cameras demonstrate efficacy in reducing traffic violations and fatalities, this research addresses a critical gap in healthcare literature regarding their broader societal and ethical consequences, including algorithmic bias, data governance failures, and privacy violations that directly impact public trust and health equity. …”
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