A Game Theoretical Priority-Aware R2V Task Offloading Framework for Vehicular Fog Networks by Kinda Khawam, Maurice Khabbaz, Joe Saad

“…Serial computation leverages a Multiple Knapsack Problem (MKP) formulation to prioritize tasks based on deadlines, while parallel computation employs a non-cooperative congestion game model. Both approaches are compared against the traditional Earliest Deadline First (EDF) scheduling algorithm. …”
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Intelligent Full-Digital Technology and Its Applications in Power Plant by Wenjian ZHANG, Geng LIANG, Gengda LI, Qingru CUI

“…In this system, the intelligent subject and digital technology are organically combined to increase and expand the breadth and depth of data information usage, i.e., expand the paths and means of information acquisition and collection to increase the breadth of information, and conduct in-depth analysis, mining and reutilization of data information by introducing more intelligent calculation algorithms and analysis methods. At the same time, the new data and knowledge generated by the system can be fed back to the existing all-digital system continuously to achieve the self-adjustment and self-organization of the system, in which the traits of intelligence are fully exhibited.…”
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Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants by Deise Cristina Dal’Ongaro, Cicero Cena, Bruno Spolon Marangoni, Daniele A. Soares-Marangoni

“…This study aimed to explore the development of a method for the diagnosis of congenital syphilis using saliva FTIR spectra and machine learning algorithms in infants aged 0 to 12 months. First, the potential of FTIR for analyzing infant saliva was evaluated. …”
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Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…This study addresses this challenge by leveraging advanced machine learning (ML) techniques to enhance the efficiency of pyrometallurgical operations such as slag optimization, composition prediction, and waste minimization. Using a combination of real-world and synthetic data, we developed models capable of both forward prediction, estimating slag and matte compositions from ore characteristics, and backward prediction, inferring ore compositions from output characteristics. …”
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Microbial methods matter: Identifying discrepancies between microbiome denoising pipelines using a leaf biofilm taphonomic dataset by Brianne Palmer, Sabina Karačić, Gabriele Bierbaum, Carole T. Gee

“…Discussion Considering the performance of denoising algorithms and the identification of amplicon sequence variants (ASVs), we recommend DADA2 for analyzing 16S rRNA and 18S rRNA. …”
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Microbiota as diagnostic biomarkers: advancing early cancer detection and personalized therapeutic approaches through microbiome profiling by Majid Eslami, Ramtin Naderian, Aisa Bahar, Ali Babaeizad, Solaleh Rezanavaz Gheshlagh, Valentyn Oksenych, Hamed Tahmasebi

“…Integrating microbiome-based diagnostics with conventional approaches, such as liquid biopsies and machine learning algorithms, could revolutionize cancer detection and management. …”
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Modeling, Simulation, and Kinetic Studies of Solvent-Free Biosynthesis of Benzyl Acetate by Vijay Kumar Garlapati, Annapurna Kumari, Paramita Mahapatra, Rintu Banerjee

“…Statistical integrated genetic algorithm optimization approach results in an optimized molar conversion of 96.32% with the predicted transesterification variables of 0.47 M alcohol molarity in a reaction time of 13.1 h, at 37.5°C using 13.31 U of immobilized lipase. …”
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Evaluation of Machine Learning Applications for the Complex Near-Critical Phase Behavior Modelling of CO₂–Hydrocarbon Systems by Daulet Magzymov, Meruyert Makhatova, Zhasulan Dairov, Murat Syzdykov

“…We compared the physical Peng–Robinson equation of state model to machine learning algorithms under varying temperatures, pressures, and composition, including challenging near-critical scenarios. …”
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The role of hematological parameters as predictors of mortality in elderly patients in the hospital period by L. V. Borisova, S. A. Rukavishnikova, A. S. Pushkin, T. A. Ahmedov, V. V. Yakovlev

“…The development of algorithms for predicting adverse outcomes, including death in elderly and senile patients, is an urgent issue. …”
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Multi-Poly-Silicon-Layer-Based Spot-Size Converter for Efficient Coupling Between Silicon Waveguide and Standard Single-Mode Fiber by Weifeng Jiang, Niharika Kohli, Xiaohan Sun, B. M. Azizur Rahman

“…The use of simple single-layer and multilayer Poly-Si-based SSCs is investigated, in which the coupling process and phase matching for isolated and composite waveguides are also carried out. The coupling loss can be reduced to 2.72 dB by using an 11-Poly-Si-layer-based SSC. …”
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Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints by Xianping Huang, Yong Chen, Wenchao Yi, Zhi Pei, Ziwen Cheng

“…To solve this problem, an improved PPO algorithm—CRGPPO-TKL—has been developed, which integrates candidate probability ratio calculations and a dynamic clipping mechanism based on target KL divergence to enhance the exploration capability and stability during policy updates. …”
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Predicting emergency department visits for non-traumatic dental-related conditions among Medicaid beneficiaries by Sung Eun Choi, Rindala Fayyad, Daniel Nolte, Christine Riedy, Sharon-Lise Normand

“…We sought to predict future “ED users” for non-traumatic dental-related conditions using machine learning algorithms and identify associated risk factors to help Medicaid outreach programs better target populations in most need of improvement in access to dental care. …”
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Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina by Džana Bašić-Čičak, Jasminka Hasić Telalović, Lejla Pašić

“…Background/Objectives: The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. …”
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Dead Sea Stromatolite Reefs: Testing Ground for Remote Sensing Automated Detection of Life Forms and Their Traces in Harsh Environments by Nuphar Gedulter, Amotz Agnon, Noam Levin

“…We collected and measured 82 field samples with an ASD spectrometer and used our spectral dataset to train three machine learning algorithms (linear regression, K-Nearest Neighbor, XGBoost). …”
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Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model by Canyu Chen, Guobin Zhu, Xiliang Chen

“…However, wetland monitoring still faces significant challenges such as high manual costs and low image monitoring accuracy, primarily due to the complex composition of wetland terrain and diverse spectral characteristics, especially located in remote suburban areas. …”
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Employing Streaming Machine Learning for Modeling Workload Patterns in Multi-Tiered Data Storage Systems by Edson Ramiro Lucas Filho, George Savva, Lun Yang, Kebo Fu, Jianqiang Shen, Herodotos Herodotou

“…Recently, different Machine-Learning (ML) algorithms have been used to model access patterns from complex workloads. …”
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Ensemble learning for prediction of inorganic scale formation: A case study in Oman by Mohammed Talib Said Al Harrasi, Alireza Kazemi, Reza Yousefzadeh

“…Thereby, the ability of six machine learning algorithms and a Power Law Ensemble Model (PLEM) to predict inorganic scale formation in carbonate formations is examined in this study. …”
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Two phase sinusoidal pulse width modulation with remodulation in the "converter – electric motor system" by A. V. Saushev, I. V. Belousov, V. F. Samoseiko

“…The results can be used in the development of algorithms for controlling frequency converters in frequency control systems of electric drives.…”
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Heterogeneous AI Music Generation Technology Integrating Fine-Grained Control by Hongtao Wang, Li Gong

“…As artificial intelligence algorithms continue to advance, researchers have increasingly harnessed their capabilities to generate music that resonates with human emotions, offering a novel means of alleviating the escalating pressures of contemporary life. …”
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Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots by Ankan Mukherjee, Anuranan Das, Adil Anwar Khan, Bhaskaran Muralidharan

“…Leveraging our model to train a machine learning algorithm, we successfully develop an automated method for the real-time detection of multiple Pauli blockade regimes. …”
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