Solving differential‐algebraic equations in power system dynamic analysis with quantum computing by Huynh T. T. Tran, Hieu T. Nguyen, Long T. Vu, Samuel T. Ojetola

“…We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.…”
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Objective monitoring of motor symptom severity and their progression in Parkinson’s disease using a digital gait device by Tamara Raschka, Jackrite To, Tom Hähnel, Stefano Sapienza, Alzhraa Ibrahim, Enrico Glaab, Heiko Gaßner, Ralph Steidl, Jürgen Winkler, Jean-Christophe Corvol, Jochen Klucken, Holger Fröhlich

“…Furthermore, we employed machine learning to evaluate whether digital gait assessments were prognostic for patient-level motor symptom progression. …”
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TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis by Gabriel Mercadal-Orfila, Gabriel Mercadal-Orfila, Piedad López Sánchez, Aranzazu Pou Alonso, Olatz Ibarra-Barrueta, Emilio Monte-Boquet, Joaquin Borrás Blasco, Nuria Padullés Zamora, Patricia Sanmartin-Fenollera, Cristina Capilla Montes, M. Ángeles Bernabéu Martínez, Salvador Herrera-Pérez

“…Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. …”
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Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination. by Derek Reiman, Godhev Kumar Manakkat Vijay, Heping Xu, Andrew Sonin, Dianyu Chen, Nathan Salomonis, Harinder Singh, Aly A Khan

“…To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. …”
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Caffeine induces age-dependent increases in brain complexity and criticality during sleep by Philipp Thölke, Maxine Arcand-Lavigne, Tarek Lajnef, Sonia Frenette, Julie Carrier, Karim Jerbi

“…We analyzed sleep electroencephalography (EEG) in 40 subjects, contrasting 200 mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. …”
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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

“…The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. …”
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Research Status and Prospects of Distribution Network Planning Technology Based on Artificial Intelligence by LI Jingru, LI Hongjun, MA Liang, JIANG Shigong, MU Chaoxu, SI Chenyi

“…It proposes potential solutions in technical research， such as graph learning， transfer learning， multimodal fusion， enhanced interpretability， and human-machine hybrid intelligence enhancement. …”
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The impact of integrated genomic surveillance on non-typhoidal Salmonella infection in Australia: an ecological studyResearch in context by Son Nghiem, Nhung Mai, My Tran, Danielle M. Cribb, Liliana Bulfone, Patiyan Andersson, Alireza Zahedi, Tuyet Hoang, Tehzeeb Zulfiqar, Angeline Ferdinand, Katie Glass, Martyn D. Kirk, Vitali Sintchenko, Amy V. Jennison, Benjamin P. Howden, Emily Lancsar

“…Results of a dynamic specification were slightly higher, with a 12.7% reduction in NTS cases after WGS. The estimated effects increased to 17.5% when a multi-period DiD model with a double machine learning estimator was applied. …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. …”
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IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…The model demonstrated a 20.0% reduction in average traffic delay and a 25.0% enhancement in traffic flow efficiency. …”
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Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…The effectiveness of SAPC is evaluated using functional requirements extracted from Software Requirement Specifications (SRS), product backlogs, and customer requests, along with a benchmark dataset for validation. Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. …”
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A Novel Approach for Enhancing Plant Leaf Classification With the Binary Cuckoo Search Algorithm by Mohammad Subhi Al-Batah, Mohammad Ryiad Al-Eiadeh, Yazan Alnsour

“…In this study, we introduced a model for classifying a variety set of plant leaves using different techniques such as factorization machine (FM), dimensionality reduction (DR), and ensemble learning (EL). …”
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Optimal design of a hybrid system composed of coagulation process and multi-stage filtration unit for on-site treatment of greywater in rural area by Zahra Akbari, Fereshteh Nourmohammadi Dehbalaei, Zahra Mohammad Hosseini, Seyed Taghi Omid Naeeni

“…Multilayer nonlinear machine learning model was utilized to evaluate the performance of this hybrid system and adaptive heuristic search algorithm was used to achieve the optimal configuration of multilayer sand filter using optimization technique. …”
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Trackformers: in search of transformer-based particle tracking for the high-luminosity LHC era by Sascha Caron, Nadezhda Dobreva, Antonio Ferrer Sánchez, José D. Martín-Guerrero, Uraz Odyurt, Roberto Ruiz de Austri Bazan, Zef Wolffs, Yue Zhao

“…One such step in need of an overhaul is the task of particle track reconstruction, a.k.a., tracking. A Machine Learning-assisted solution is expected to provide significant improvements, since the most time-consuming step in tracking is the assignment of hits to particles or track candidates. …”
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From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers by Emmanuel A. Onsay, Jomar F. Rabajante

“…By training and testing datasets, this work proposes new metrics and illustrates the effectiveness of machine learning in predicting poverty. Lastly, the results provide various localities with customized policy targeting tools for poverty alleviation. …”
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Sleep Fragmentation as a Diagnostic Biomarker of Traumatic Brain Injury by Grant S. Mannino, Christian R. Baumann, Mark R. Opp, Rachel K. Rowe

“…Drawing on empirical findings from a mouse model of diffuse TBI, we show that summary measures of sleep fragmentation and duration can reliably distinguish injured from uninjured animals using dimensionality reduction and machine learning techniques. …”
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An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysi... by Debo He, Zhixin Dong, Bo Zhu

“…In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. …”
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Adaptive Handover Management in High-Mobility Networks for Smart Cities by Yahya S. Junejo, Faisal K. Shaikh, Bhawani S. Chowdhry, Waleed Ejaz

“…Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios.…”
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Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation by Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Leondry Mayeta-Revilla, Eduardo P. Cavieres, Eduardo P. Cavieres, Eduardo P. Cavieres, Matías Salinas, Matías Salinas, Matías Salinas, Diego Mellado, Diego Mellado, Diego Mellado, Diego Mellado, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Sebastian Ponce, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Francisco Torres Moyano, Steren Chabert, Steren Chabert, Steren Chabert, Marvin Querales, Marvin Querales, Julio Sotelo, Rodrigo Salas, Rodrigo Salas, Rodrigo Salas

“…Dimensionality reduction is performed using machine learning, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to produce interpretable decision rules. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…Compared with traditional machine learning and deep learning models, the improved model better captures domain-specific information, resulting in an R² increase of 0.23 and reductions in RMSE and MAE by 0.016 and 0.014, respectively. …”
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