Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction by Nor Irwin Basir, Kathlyn Kaiyun Tan, Danny Hartanto Djarum, Zainal Ahmad, Dai-Viet N. Vo, Zhang Jie

“…The findings confirm that both autoencoder models enhance API prediction accuracy, with the deep sparse autoencoder emerging as the optimal model, highlighting the potential of deep learning in improving air quality prediction. ABSTRAK: Pencemaran udara, merupakan satu cabaran global yang didorong oleh perindustrian, urbanisasi pesat, dan pertumbuhan populasi, adalah disebabkan oleh pelepasan gas, partikel, dan molekul biologi merbahaya ke atmosfera, menimbulkan risiko serius kepada kesihatan dan alam sekitar. …”
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Anti-TROVE2 Antibody Determined by Immune-Related Array May Serve as a Predictive Marker for Adalimumab Immunogenicity and Effectiveness in RA by Po-Ku Chen, Joung-Liang Lan, Yi-Ming Chen, Hsin-Hua Chen, Shih-Hsin Chang, Chia-Min Chung, Nurul H. Rutt, Ti-Myen Tan, Raja Nurashirin Raja Mamat, Nur Diana Anuar, Jonathan M. Blackburn, Der-Yuan Chen

“…The biomarkers were identified for predicting ADAb development and therapeutic response using the immune-related microarray and machine learning approach. ADAb-positive patients had lower drug levels at week 24 (median=0.024 μg/ml) compared with ADAb-negative patients (median=6.38 μg/ml, p<0.001). …”
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Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity by Xiaochen Tang, Yanyan Wei, Jiaoyan Pang, Lihua Xu, Huiru Cui, Xu Liu, Yegang Hu, Mingliang Ju, Yingying Tang, Bin Long, Wei Liu, Min Su, Tianhong Zhang, Jijun Wang

“…Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. …”
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Aqueous foams in microgravity, measuring bubble sizes by Pasquet, Marina, Galvani, Nicolo, Pitois, Olivier, Cohen-Addad, Sylvie, Höhler, Reinhard, Chieco, Anthony T., Dillavou, Sam, Hanlan, Jesse M., Durian, Douglas J., Rio, Emmanuelle, Salonen, Anniina, Langevin, Dominique

“…Extracting the bubble size distribution from images of a foam surface is difficult so we have used three different procedures: manual analysis, automatic analysis with a customized Python script and machine learning analysis. Once various pitfalls were identified and taken into account, all the three procedures yield identical results within error bars. …”
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Are neurasthenia and depression the same disease entity? An electroencephalography study by Ge Dang, Lin Zhu, Chongyuan Lian, Silin Zeng, Xue Shi, Zian Pei, Xiaoyong Lan, Jian Qing Shi, Nan Yan, Yi Guo, Xiaolin Su

“…The demographic and clinical characteristics, EEG power spectral density, and functional connectivity were compared between the neurasthenia and MDD groups. Machine Learning methods such as random forest, logistic regression, support vector machines, and k nearest neighbors were also used for classification between groups to extend the identification that there is a significant different pattern between neurasthenia and MDD. …”
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Blockade of IL-17A/IL-17R Pathway Protected Mice from Sepsis-Associated Encephalopathy by Inhibition of Microglia Activation by Bo Ye, Tianzhu Tao, Andong Zhao, Liyuan Wen, Xiaofei He, Yi Liu, Qiang Fu, Weidong Mi, Jingsheng Lou

“…Septic peritonitis induced significant impairment of learning memory and exploratory activity, which was associated with a higher expression of IL-17A, IL-1β, and TNF-α in the brain homogenate. …”
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Providing a General Model for the Successful Implementation of Digital Transformation in Organizations by Haidar Ahmadi, Najme Parsaei, Seyyed Hamed Hashemi, Hamidreza Nematollahi

“…Conclusion Digital transformation extends beyond the mere adoption of emerging technologies such as artificial intelligence and machine learning; it represents a paradigm shift in how traditional management and operational practices are conducted across various functions, including product development, engineering, marketing, sales, and service delivery. …”
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Omega-3 Fatty Acids: Key Players in Cognitive Function and Brain Health by Wiktoria Wesołowska, Emilia Bachoń, Michalina Doligalska, Aleksandra Stremel, Agnieszka Leszyńska, Julia Linke, Zuzanna Bałoniak, Dominika Kozłowska, Julia Bałoniak, Weronika Tuszyńska

“…Results and conclusions: Research shows that DHA and EPA supplementation can significantly improve memory and learning, especially in older individuals and those with cognitive impairments. …”
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Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study. by Elizabeth Ajalo, David Mukunya, Ritah Nantale, Frank Kayemba, Kennedy Pangholi, Jonathan Babuya, Suzan Langoya Akuu, Amelia Margaret Namiiro, Yakobo Baddokwaya Nsubuga, Joseph Luwaga Mpagi, Milton W Musaba, Faith Oguttu, Job Kuteesa, Aloysius Gonzaga Mubuuke, Ian Guyton Munabi, Sarah Kiguli

“…<h4>Background</h4>Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. …”
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Identifying the causes and consequences of pregnancy in Iranian Kurdish women under the age of 18: A grounded theory study by Javad Yoosefi lebni, Ahmad Ahmadi, Seyed Fahim Irandoost, Mandana Saki, Hossein Safari, Nafiul Mehedi

“…Results: After the data analysis and coding process, the conceptual model of causes and consequences of pregnancy in adolescents emerged, including 1) predisposing conditions (sociocultural factors: Social learning, misconceptions about fertility and childbirth, preventing stigma), 2) causal conditions (individual factors: lack of knowledge on how to prevent pregnancy, improper use of contraceptives, inadequate knowledge about the risks of pregnancy in adolescence, fear of the side effects of using contraceptives, filling the vacuum of loneliness, family factors: husband's and his family's pressure, committing her husband to life, consolidating her position in family), 3) intervening conditions (structural factors: no barriers to pregnancy, difficult access to contraceptives), 4) strategies and interactions (positive reactions: trying to prepare herself for raising a child, taking better care of herself and her child, negative reactions: trying to kill herself and the kid, fear and concealment), and 5) consequences (destructive consequences: threats to the health of the child, threat to mother's health, inadequate access to health services, constructive consequences: increase of support, strengthen the sense of empowerment). …”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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DIDATTICA DEL LESSICO: UN PERCORSO BIBLIOGRAFICO by Viviana de Leo

“…These contributions are particularly relevant for applications in the teaching and learning of both first (L1) and additional languages (L2). 2. …”
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A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs by Qihang Xie, Qihang Xie, Xuefei Li, Yuanyuan Li, Yuanyuan Li, Jiayi Lu, Jiayi Lu, Shaodong Ma, Yitian Zhao, Yitian Zhao, Jiong Zhang, Jiong Zhang

“…However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context.MethodsTo address these issues, this study introduces M3B-Net, a novel multi-modal, multi-branch framework that leverages fundus fluorescence angiography (FFA) images to improve retinal vessel segmentation in UWF fundus images. …”
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Genetic Biomarkers and Circulating White Blood Cells in Osteoarthritis: A Bioinformatics and Mendelian Randomization Analysis by Yimin Pan, Xiaoshun Sun, Jun Tan, Chao Deng, Changwu Wu, Georg Osterhoff, Nikolas Schopow

“…The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. …”
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Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects by Umar Hayat, Cao Ke, Lirong Wang, Gengrui Zhu, Weichao Fang, Xinwei Wang, Changwen Chen, Yong Li, Jinlong Wu

“…This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. …”
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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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A User-Centered Design Approach for a Screening App for People With Cognitive Impairment (digiDEM-SCREEN): Development and Usability Study by Michael Zeiler, Nikolas Dietzel, Fabian Haug, Julian Haug, Klaus Kammerer, Rüdiger Pryss, Peter Heuschmann, Elmar Graessel, Peter L Kolominsky-Rabas, Hans-Ulrich Prokosch

“…The test was administered using different randomization options to minimize learning effects. digiDEM-SCREEN was developed as a tablet and smartphone app. …”
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Emotion regulation-based internet-delivered cognitive behavioural therapy for premenstrual dysphoric disorder: study protocol for a randomised controlled trial in Sweden by Filipa Sampaio, Alkistis Skalkidou, Gerhard Andersson, Cornelia Weise, Johanna M. Hoppe, Maria Kleinstaeuber, Johan Vegelius, Erika Comasco, Maria Gröndal, Elisavet Kaltsouni, Felicia Sundström, Monica Buhrman

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Model Evaluasi Usability Menggunakan Confirmatory Factor Analysis pada KRS Online by Endah Ratna Arumi, Pristi Sukmasetya, Agus Setiawan

“…Usability evaluation is the focus of the assessment by users of the system to find out how easy to learn and use the system. This research aims to find valid models from several usability models that are analyzed using Confirmatory Factor Analysis (CFA). …”
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Metabolic reprogramming and macrophage expansion define ACPA-negative rheumatoid arthritis: insights from single-cell RNA sequencing by Yafeng Jiang, Zhaolan Hu, Roujie Huang, Kaying Ho, Pengfei Wang, Jin Kang, Jin Kang

“…By integrating single-cell RNA sequencing with machine learning, this study established a neural network model that robustly differentiates patients with ACPA− RA from healthy controls, highlighting promising diagnostic biomarkers and therapeutic targets centered on immune cell metabolism.…”
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