Energy Consumption Optimization for an Electric Delivery Vehicle by Andrzej Łebkowski

“…This work presents the structure of the optimizer system and the developed control algorithms. Additionally, electric motor excitation control was used, which may have contributed to reducing the power and weight of the electric drive motor. …”
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The Transformative Impact of Artificial Intelligence in Healthcare: Innovations, applications, and future prospects by Shen Ruoyu

“…At the same time, the application of AI in healthcare poses a number of challenges, such as data privacy, algorithmic bias, and ethics. This study also explores the role of AI in global health, the need for policies and regulations, and the long-term impact of AI on future healthcare systems. …”
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The tumour histopathology "glossary" for AI developers. by Soham Mandal, Ann-Marie Baker, Trevor A Graham, Konstantin Bräutigam

“…By providing this essential knowledge to the computational community, we aim to accelerate the development of AI algorithms for cancer research.…”
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Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry by Bowen Cai

“…Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.…”
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Exploring Artificial Intelligence for Enhanced Endodontic Practice: Applications, Challenges, and Future Directions by Santosh R. Patil, Mohmed Isaqali Karobari

“…However, challenges such as data privacy issues, algorithmic biases, and integration barriers must be addressed to enable broader adoption. …”
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Dagnostics of hydraulic drive of airfield equipment by I. V. Leskovets, A. A. Grats, V. D. Rogozhyn

“…The directions of diagnosis are outlined: with the applications of algorithms based on the use of a complex of controllers designed to collect and store up-to-date diagnostic information; with the help of artificial intelligence allowing to perform step-by-step diagnostics on current information about the speed of hydraulic motors, pressure and fluid velocity. …”
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Investigating the hydrogen renaissance in the global energy transition with AI integration by Abderrahim Lakhouit

“…AI plays a pivotal role in optimizing hydrogen production methods, such as electrolysis, by enhancing process efficiencies through machine learning models that predict and optimize operational parameters. In storage, AI-driven algorithms are improving the management of hydrogen in large-scale storage systems, helping to mitigate issues such as leakage and optimizing storage conditions. …”
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Gravity experiments with radio pulsars by Paulo C. C. Freire, Norbert Wex

“…New telescopes, instrumentation, timing and search algorithms promise a significant improvement of the existing tests and the discovery of (qualitatively) new, more relativistic binary systems.…”
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Endemic cushions of the Khorassan-Kopet Dagh floristic province show differential responses to future climate change by Zohreh Atashgahi, Mohammad Bagher Erfanian, Hamid Moazzeni, Gelareh Shemirani, Atefeh Pirani

“…The present study investigates the effects of climate change on the distribution of endemic cushion species in the Khorassan-Kopet Dagh (KK) floristic province, the eastern-most part of the Irano-Anatolian Biodiversity Hotspot. We predicted the current and future range of 19 cushions in 2040 and 2100, using 19 bioclimatic layers along with two different SSPs and an ensemble of 12 modeling algorithms. …”
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Phylogenetic and functional assessment of orthologs inference projects and methods. by Adrian M Altenhoff, Christophe Dessimoz

“…We systematically compared their predictions with respect to both phylogeny and function, using six different tests. …”
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Chemoresistance factors in non-small cell lung cancer by А. I. Shevchenko, А. P. Коlesnik, А. V. Каdzhoian, V. А. Кuzmenko

“…Basing on current data we tried to analyze prognostic and predictive value of main chemoresistance factors in NSCLC. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance. …”
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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. …”
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Personalized medication recommendations for Parkinson’s disease patients using gated recurrent units and SHAP interpretability by Atiye Riasi, Mehdi Delrobaei, Mehri Salari

“…This study aims to demonstrate the potential of sequence-by-sequence algorithms in recommending personalized medication combinations for patients with PD based on their previous visits. …”
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Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS. by Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, Amit Sheth

“…Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. …”
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An Immune-Related Prognostic Classifier Is Associated with Diffuse Large B Cell Lymphoma Microenvironment by Xiao-Jie Liang, Rui-ying Fu, He-nan Wang, Jing Yang, Na Yao, Xin-di Liu, Liang Wang

“…The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. …”
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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

“…Reinforcement Learning (RL) in CRs helps predict spectral changes and identify optimal transmission frequencies. …”
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Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria by Adamu Muhammad Ibrahim, Mohamed Mustaf Ahmed, Shuaibu Saidu Musa, Usman Abubakar Haruna, Mohammed Raihanatu Hamid, Olalekan John Okesanya, Aishat Muhammad Saleh, Don Eliso Lucero-Prisno III

“…AI technologies, including predictive modeling and ML algorithms such as random forests and convolutional neural networks (CNNs), can analyze diverse data sources—such as meteorological, environmental, and health records—to detect patterns and predict outbreaks. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Integrated analysis showed that ATRX is positively correlated with STING in high-grade glioma (HGG) with wild-type ATRX and IDH1. Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. …”
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Comparing the potential of tree-based and area-based forest height metrics for aboveground biomass estimation in complex forest landscapes by Weiyan Liu, Yuling Chen, Haitao Yang, Guangcai Xu, Shiyu Yan, Lingjun Li, Ling Chen, Qinghua Guo

“…Two modeling approaches—parametric mixed-effects models (MEM) and non-parametric machine learning (ML) algorithms—were applied to evaluate predictive accuracy. …”
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