A Survey and Comparison the Amount of Important Pollutants in the Exhaust of Light Gasoline Vehicles Referring to the Technical Inspection Centers in Urban Areas: A Case Study in I... by Abbas Khodabakhshi, Moluk Hadi Alijanvand, Fazel Mohammadi-Moghadam, Abdullah Mutauligalleh kolaie, Soghra Ebrahimi

“…The audit analysis, which involved a linear combination of O2, CO, CO2, HC, and vehicle age, successfully differentiated between cars in Shahrekord and Rasht, yielding a correct prediction rate of 81.3% (Wilks’ Lambda statistic = 0.687, Eigen value = 0.455, Canonical correlation value = 0.60 with a P < 0.0001). …”
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Thermal-Moisture Dynamics at Different Underlying Surfaces in Permafrost Regions of the Central Tibetan Plateau by considering the Effect of Rainfall by Bingbing Lei, Bin Wang, Mingli Zhang, Zhixiong Zhou, Guang Li, Guodong Yue

“…The results can provide theoretical and simulated guidance for the stability prediction and analysis of various underlying surfaces in the central QTP where rainfall is increasing.…”
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Finnish national patient data repository as data source for FINRISK risk calculator by Viljami Männikkö, Henna Kujanen, Joona Munukka, Klaus Förger

“…Risk calculation based on the Kanta PDR could enable monitoring of predicted CVD risks at the level of the Finnish population and targeting of preventive healthcare to high-risk individuals. …”
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Controlling the formation of microstructure at the melt-pool boundaries during directed energy deposition of aluminum alloy with a modified continuous growth restriction factor by Shanshan Xu, Bo Yin, Jiale Wang, Liquan Jin, Yu Yin, Zhenhua Li, David H. StJohn, Petro Pavlenko, Yueling Guo

“…However, existing analytical models fail to consider internal and external factors, particularly the solute concentration and scan speed, leading to low prediction accuracy of the solidified microstructure. …”
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Distribution and clinicopathological characteristics of G-CSF expression in tumor cells and stromal cells in upper tract urothelial carcinoma by Go Kobayashi, Yohei Sekino, Hikaru Nakahara, Kohei Kobatake, Keisuke Goto, Tetsutaro Hayashi, Kazuhiro Sentani, Nobuyuki Hinata

“…A prognostic model was constructed by incorporating the presence or absence of G-CSF expression along with clinicopathologic factors, which allowed for a more accurate prediction of poor prognosis. We further showed that G-CSF expression was associated with a high Ki-67 labeling index and with PD-L1, HER2, and p53 expression in UTUC. …”
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Evaluating Radio Occultation (RO) Constellation Designs Using Observing System Simulation Experiments (OSSEs) for Ionospheric Specification by Nicholas Dietrich, Tomoko Matsuo, Chi‐Yen Lin, Brandon diLorenzo, Charles Chien‐Hung Lin, Tzu‐Wei Fang

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A Dual-Stage Processing Architecture for Unmanned Aerial Vehicle Object Detection and Tracking Using Lightweight Onboard and Ground Server Computations by Odysseas Ntousis, Evangelos Makris, Panayiotis Tsanakas, Christos Pavlatos

“…The UAV transmits selected frames to the ground server, which handles advanced tracking, trajectory prediction, and target repositioning using state-of-the-art deep learning models. …”
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Mapping the Landscape of AI-Driven Human Resource Management: A Social Network Analysis of Research Collaboration by Mehrdad Maghsoudi, Motahareh Kamrani Shahri, Mehrdad Agha Mohammad Ali Kermani, Rahim Khanizad

“…The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. …”
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Association of Septic Shock with Mortality in Hospitalized COVID-19 Patients in Wuhan, China by Shaoqiu Chen, Zitong Gao, Ling Hu, Yi Zuo, Yuanyuan Fu, Meilin Wei, Emory Zitello, Gang Huang, Youping Deng

“…Log-rank test was conducted to determine any association with clinical progression. A prediction model was established using random forest. …”
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Use of the LCM Model in the prospective simulation of land use in the Nafoun watershed (Northern Ivory Coast) by Kamagate Anzoumanan, Koffi Ehouman Serge, Koffi Yao Blaise

“…Landsat images from 1986, 2000 and 2023 were used to assess and predict the spatio- temporal distributions of land use change. …”
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Improved Outcome of Severe Acute Pancreatitis in the Intensive Care Unit by Polychronis Pavlidis, Siobhan Crichton, Joanna Lemmich Smith, David Morrison, Simon Atkinson, Duncan Wyncoll, Marlies Ostermann

“…On admission to ICU, the median Acute Physiology and Chronic Health Evaluation (APACHE) II score was 17, the pancreatitis outcome prediction score was 8, and the median Computed Tomography Severity Index (CTSI) was 4. …”
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Magnetic field influence on heat transfer of NEPCM in a porous triangular cavity with a cold fin and partial heat sources: AI analysis combined with ISPH method by Munirah Aali Alotaibi, Weaam Alhejaili, Abdelraheem M. Aly, Samiyah Almalki

“…The ANN model’s training, reflected in a decreasing mean squared error (MSE), demonstrates prediction accuracy, and regression analysis reveals high model reliability, with predictions closely aligning with theoretical Nu̅ values.…”
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Sustainability metrics targeted optimization and electric discharge process modelling by neural networks by Muhammad Sana, Muhammad Asad, Muhammad Umar Farooq, Mehdi Tlija, Rodolfo Haber

“…An artificial neural network (ANN) has been constructed for the better prediction of output responses. Moreover, multi-response optimization through the non-dominated sorting genetic algorithm (NSGA-II) has also been performed. …”
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Deep Ensemble Learning for Human Action Recognition in Still Images by Xiangchun Yu, Zhe Zhang, Lei Wu, Wei Pang, Hechang Chen, Zhezhou Yu, Bin Li

“…Finally, we design the deep ensemble learning based on voting strategy (DELVS) model to pool together multiple deep models with weighted coefficients to obtain a better prediction. More importantly, the model complexity can be reduced by lessening the number of trainable parameters, thereby effectively mitigating overfitting issues of the model in small datasets to some extent. …”
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High-resolution canopy fuel maps based on GEDI: a foundation for wildfire modeling in Germany by Johannes Heisig, Milutin Milenković, Edzer Pebesma

“…Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. …”
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Deregulation mechanisms and therapeutic opportunities of p53-responsive microRNAs in diffuse large B-cell lymphoma by Elena N. Voropaeva, Yuriy L. Orlov, Anastasia B. Loginova, Olga B. Seregina, Vladimir N. Maksimov, Tatiana I. Pospelova

“…The understanding of the effect of p53-responsive microRNA dysregulation on oncogenesis achieved in recent decades opens wide opportunities for the diagnosis, prediction and of microRNA-based cancer therapy. Development of new bioinformatics tools and databases for microRNA supports DLBCL research. …”
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Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes by S. Ubbiali, C. Kühnlein, C. Schär, L. Schlemmer, T. C. Schulthess, T. C. Schulthess, M. Staneker, H. Wernli

“…In order to verify GT4Py for numerical weather prediction (NWP) systems, we put additional emphasis on the implementation and validation of the tangent-linear and adjoint model versions which are employed in data assimilation. …”
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Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach by Jooseung Lee, Jimyung Kang, Sangwoo Son, Hui-Myoung Oh

“…To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. …”
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Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models by Matheus Dalmolin, Karolayne S. Azevedo, Luísa C. de Souza, Caroline B. de Farias, Martina Lichtenfels, Marcelo A. C. Fernandes

“…To mitigate these challenges, the SHAP (Shapley Additive Explanations) method was applied to generate a list of features, aiming to understand which characteristics influenced the models’ decision-making processes and, consequently, the prediction results for the five tumor types. The SHAP analysis identified 119, 80, and 10 genes for the RF, XGB, and DT models, respectively, totaling 209 genes, resulting in 172 unique genes. …”
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Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load D... by Hafeez Ur Rehman Siddiqui, Robert Brown, Adil Ali Saleem, Muhammad Amjad Raza, Sandra Dudley

“…These results highlight the proposed framework&#x2019;s potential for real-world implementation in modern power grids, offering enhanced fault prediction and resilience. This research establishes a pathway for integrating advanced data augmentation and machine learning techniques into operational power grid systems, ensuring stability and reliability.…”
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