Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…A machine learning tool is exploited to discover and characterize high entropy alloys with satisfying targets. …”
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Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images by Feifeng Liu, Weihu Wang

“…Based on the introduction of the partial differential equation image filtering method, through the parallel supervised learning of aesthetic attribute labels, this paper extracts the global aesthetic depth features, adopts the partial differential equation to evolve the contour C constant, and constructs a convolution neural network. …”
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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). …”
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Rapid detection of honey adulteration using machine learning on gas sensor data by Mehmet MİLLİ, Nursel SÖYLEMEZ MİLLİ, İsmail Hakkı PARLAK

“…The sensor captures the gas composition of honey mixtures, creating a unique digital fingerprint that can be analysed using machine learning techniques. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Additionally, we use meteorological data from a set of sensors installed at a meteorological station located in Belém to train multivariate statistical and machine learning (ML) models to predict precipitation. Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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Resilient dispatching optimization of power system driven by deep reinforcement learning model by Haifeng Zhang, Yifu Zhang, Jiajun Zhang, Xiangdong Meng, Jiazu Sun

“…In this study, we propose an innovative power system scheduling strategy based on Deep Reinforcement Learning (DRL). The approach combines large-scale historical data with advanced predictive models to create a well-designed decision-making framework. …”
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Hierarchical multi-instance multi-label learning for Chinese patent text classification by Yunduo Liu, Fang Xu, Yushan Zhao, Zichen Ma, Tengke Wang, Shunxiang Zhang, Yuhao Tian

“…To further enhance the accuracy of the Chinese patent classification, this paper proposes a model, based on the patent structure and takes the patent claim as subjects, with multi-instance multi-label learning as the main method. …”
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Probiotic potential of Phocaeicola coprocola in modulating learning and memory behaviors in the honeybee model by Mengqi Xu, Mengqi Xu, Xiaohan Zhang, Xiaohan Zhang, Xi Luo, Guanzhou Zhou, Guanzhou Zhou, Nana Zhang, Xiaoyan Chi, Xiaoyan Chi, Rongrong Ren, Lihua Peng, Gang Sun, Yunsheng Yang, Yunsheng Yang

“…Gut microbial composition was analyzed using full-length 16S rRNA gene sequencing based on PacBio SMRT technology, and metabolic profiling was conducted using untargeted LC–MS/MS analysis.ResultsP. coprocola supplementation significantly improved cognitive performance, with learning success rates of 74.13% in the treatment group versus 50.85% in controls (p = 0.0093). …”
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Features of Distance Learning in Higher Education Institutions in The Context of The Covid-19 Pandemic by Oksana N. Goncharova, Milera Yu. Halilova

“…The purpose of the research is to study the quality of distance learning. The paper identifies the main problems that arose during the transition to distance learning due to the epidemiological situation in 2020-2021 in the Russian Federation.Materials and methods. …”
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A Next-generation Exoplanet Atmospheric Retrieval Framework for Transmission Spectroscopy (NEXOTRANS): Comparative Characterization for WASP-39 b Using JWST NIRISS, NIRSpec PRISM,... by Tonmoy Deka, Tasneem Basra Khan, Swastik Dewan, Priyankush Ghosh, Debayan Das, Liton Majumdar

“…This hybrid approach enables a comparison between traditional Bayesian methods and computationally efficient machine learning techniques. …”
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METHODICAL ASPECTS OF USING VIRTUAL MUSEUMS OF THE EDUCATIONAL PROCESS IN GENERAL EDUCATION INSTITUTIONS by Наталія Сороко

“…For the use of virtual museums in the educational process to promote the development and formation of cognitive interests of students, the teacher must: select information to be presented with the help of virtual museum, by content and compose it in a composition so that it corresponds to the purpose, age, knowledge, and interests  of  students;  the  use  heuristic  methods  in  teaching  educational material with the help of virtual museum; to organize by the specific interests of students various forms of independent work with the use of virtual museum (preferably in the form of educational projects).…”
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Optimal mixture design for organic Rankine cycle using machine learning algorithm by Valerio Mariani, Saverio Ottaviano, Davide Scampamorte, Andrea De Pascale, Giulio Cazzoli, Lisa Branchini, Gian Marco Bianchi

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In‐Situ Rheology Measurements via Machine‐Learning Enhanced Direct‐Ink‐Writing by Robert D. Weeks, Jennifer M. Ruddock, J. Daniel Berrigan, Jennifer A. Lewis, James. O. Hardin

“…Direct ink writing, an extrusion‐based 3D printing method, is well suited for high‐mix low‐volume manufacturing. …”
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Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences. by Waqar Ahmad, Abdul Raheem Shahzad, Muhammad Awais Amin, Waqas Haider Bangyal, Tahani Jaser Alahmadi, Saddam Hussain Khan

“…The methodology we implement is based on the utilisation of reliable methods for extracting features, namely Di-peptide Composition (DPC), Amino Acid Composition (AAC), and Pseudo amino acid composition (Pse-AAC). …”
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“Three‐in‐one” Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning by Yidan Wang, Jiazhu Sun, Jiuhong Yi, Ruijie Fu, Ben Liu, Yunlei Xianyu

“…With the aid of machine learning, the urine composition is precisely detected for the diagnosis of bladder cancer. …”
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Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations by Adi Hatav, Yelena Vysotski, Anna Shapira, Shiri Procaccia, David Meiri, Dvir Aran

“…Methods In a prospective study of 329 chronic pain patients (40% females; aged 48.9 ± 15.5) prescribed medical cannabis, we examined whether the chemical composition of cannabis cultivars could predict treatment outcomes. …”
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Machine Learning Reveals Microbial Taxa Associated with a Swim across the Pacific Ocean by Garry Lewis, Sebastian Reczek, Osayenmwen Omozusi, Taylor Hogue, Marc D. Cook, Jarrad Hampton-Marcell

“…<b>Purpose:</b> This study aimed to characterize the association between microbial dynamics and excessive exercise. <b>Methods:</b> Swabbed fecal samples, body composition (percent body fat), and swimming logs were collected (n = 94) from a single individual over 107 days as he swam across the Pacific Ocean. …”
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Microbial community diversity in the soil of Barrientos Island estimated by RAPD and Biolog Ecoplate methods by Learn-Han Lee, Vengadesh Letchumanan, Nurul-Syakima Ab Mutalib, Yoke Kqueen Cheah

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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…The engineering characteristics encompassed Los Angeles abrasion value, aggregate crushing value, aggregate impact value, specific gravity, water absorption, and unconfined compressive strength, whereas petrographic examination of thin sections quantified the mineralogical composition. Statistical methods and machine learning models have been applied to elucidate the relationships between the petrographic and engineering features of the aggregates and establish potential predictive capability. …”
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Prediction of virus-host associations using protein language models and multiple instance learning. by Dan Liu, Francesca Young, Kieran D Lamb, David L Robertson, Ke Yuan

“…It also identifies important viral proteins that significantly contribute to host prediction. The method combines a pre-trained large protein language model (ESM) and attention-based multiple instance learning to allow protein-orientated predictions. …”
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