Characterization of Ultra-Short plasma Cell-Free DNA in maternal blood and its preliminary potential as a screening marker for preeclampsia by Weiqiang Liu, Qin Lu, Weijie Xie, Liang Hu, Lijuan Wen, Shuxian Zeng, Jiatong Zhong, Nani Lin, Yanxiang Chen, Yimin Wang

“…Based on these features, the diagnostic model achieved an area under the curve (AUC) of 0.90 in the training cohort and 0.86 in the test cohort. …”
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A model to simulate human cardio-respiratory responses to different fluid resuscitation treatments after hemorrhagic injury by Varghese Kurian, Varghese Kurian, Xin Jin, Xin Jin, Sridevi Nagaraja, Sridevi Nagaraja, Anders Wallqvist, Jaques Reifman

“…Decision-support systems based on artificial intelligence and machine learning algorithms can enhance the capability and capacity of medics to provide care for combat casualties during large-scale combat operations. …”
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Application of deep learning for coherent pixel selection in time series InSAR for urban area and transport infrastructure monitoring by S. Azadnejad, A. Kandiri, A. Hrysiewicz, F. O’Loughlin, E.P. Holohan, S. Dev, S. Donohue

“…Unlike previous complex deep learning approaches to this problem, a multi-layer perceptron (MLP) model is trained on time-domain and frequency-domain features extracted from a time series of SAR amplitude images to predict the temporal coherence value of each individual pixel, and thus to select coherent pixels. …”
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A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines by MD Azam Khan, Arifur Rahman, Farhad Uddin Mahmud, Kanchon Kumar Bishnu, Hadiur Rahman Nabil, M. F. Mridha, Md. Jakir Hossen

“…A comparative study with ten baseline models, including Long Short-Term Memory (LSTM), Transformer-based models, and traditional machine learning classifiers, demonstrates that the PINN-BNN model outperforms existing approaches while maintaining computational efficiency with a training time of 39.8 minutes and an inference time of 1.7 ms per sample. …”
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A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model by Yujie Wang, Yujie Wang, Can Liu, Yinghan Fan, Chenyue Niu, Wanyun Huang, Yixuan Pan, Jingze Li, Jingze Li, Yilin Wang, Jun Li, Jun Li, Jun Li

“…Bacterial and viral pneumonia share many similar clinical features, thus making diagnosis a challenging task. …”
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Recruitment for Voluntary Video and Mobile HIV Testing on Social Media Platforms During the COVID-19 Pandemic: Cross-Sectional Study by Piao-Yi Chiou, Wei-Wen Tsao, Chia-Lin Li, Jheng-Min Yu, Wen-Han Su, Zhi-Hua Liu, Cheng-Ru He, Yu-Chun Chang, Yi-Hsuan Tsai

“…After one-on-one message discussions through the platforms, the well-trained research assistants provided mobile or video VCT based on the participants’ availability. …”
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Forecasting Electric Vehicle Charging Demand in Smart Cities Using Hybrid Deep Learning of Regional Spatial Behaviours by Muhammed Cavus, Huseyin Ayan, Dilum Dissanayake, Anurag Sharma, Sanchari Deb, Margaret Bell

“…The framework is trained on user-level survey data from two demographically distinct UK regions, the West Midlands and the North East, incorporating user demographics, commute distance, charging frequency, and home/public charging preferences. …”
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Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images by Saad Islam, Ravinesh C. Deo, Prabal Datta Barua, Jeffrey Soar, U. Rajendra Acharya

“…Our methodology includes image preprocessing pipelines for each modality, custom convolutional neural network/ResNet-based architectures for single-modality analysis, and a two-branch fusion network combining fundus and optical coherence tomography feature representations. …”
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Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

“…A machine learning model based on extreme gradient boosting (XGBoost) was trained to predict 2-year MACE using 10-fold cross-validation, incorporating GDF15 and clinical variables including age, sex, comorbidities (hypertension, diabetes, dyslipidemia, congestive heart failure, coronary artery disease, and previous stroke or transient ischemic attack), smoking history, and cardioprotective medication use. …”
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Visibility, democratic public space and socially inclusive cities by Ceren Sezer

“…It would be especially valuable in cases of profound neighbourhood transformation processes, which modify the demographic profile of a neighbourhood. Finally, training and education of designers and planners of public space should incorporate visibility as an important concept to examine the diversity and vitality features of public space, in order to promote democratic streets and more socially inclusive cities. …”
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Detection and mapping of Antarctic lichen using drones, multispectral cameras, and supervised deep learning by Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Diana King, Elka Blackman, Johan Barthelemy, Barbara Bollard, Sharon A. Robinson, Felipe Gonzalez

“…Two DL methods were evaluated to classify and map Usnea spp., Umbilicaria and Pseudephebe species (black lichen), moss and non-vegetation: method (1) standalone DL model fitting, namely fully convolutional network (FCN), U-Net, and Deeplabv3+, with semi-automatic labelling thresholding using VIs; and method (2) ensemble stacking by using eXtreme gradient boosting (XGBoost) as the input model, whose predictions are used as features for training a U-Net model. In Method 1, U-Net exhibited the best performance over the other models. …”
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Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images by Ke Zhang, Youhui Qiu, Songwei Feng, Han Yin, Qi Liu, Yuxin Zhu, Haoyu Cui, Xiaoying Wei, Guoqing Wang, Xiangxue Wang, Yang Shen

“…These three subsets of HRP patients were combined with the HRD patients to establish three new training groups for subsequent model construction. …”
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Point-of-caRE DiagnostICs for respiraTOry tRact infectionS (PREDICTORS) study: developing guidance for using C-reactive protein point-of-care tests in the management of lower respi... by Tom Fahey, Carmel Hughes, Akke Vellinga, Cristin Ryan, Gerard J Molloy, Cathal Cadogan, Joseph O’Shea

“…The aim of this study was to develop best practice guidance for using CRP POCT in the management of LRTIs in primary care.Design Scoping review findings informed guidance statements, which were then evaluated through a three-round Delphi process with an expert panel via web-based questionnaires. Statements focused on intended use, detection of bacterial LRTIs, communication strategies, device features, performance and ease of use of CRP POCT.Setting and participants The panel of experts included 19 healthcare professionals across several specialties, including general practitioners, community pharmacists, hospital pharmacists and respiratory physicians.Main outcome measures Panellists rated each guidance statement using a 5-point Likert scale, with acceptance, revision or rejection determined using predefined cut-off scores for medians and interquartile ranges. …”
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Acute Respiratory Infections Identification With Cough Sounds and Overlapping Patch Modulated Vision Transformers by P. V. V. Kishore, D. Anil Kumar, Pasupuleti Sasikiran, Kaja Krishna Mohan, P. Praveen Kumar, Mogadala Vinod Kumar

“…Automatic cough identification is being conducted using speech frequency analysis and machine learning models. Learning models trained on Mel frequency spectrum(MFCC) features of cough sounds represented as images have recorded an average binary classification accuracy of 68%. …”
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COOPERATION BETWEEN THE MINISTRY OF NATIONAL EDUCATION AND LOCAL SELF-GOVERNMENT BODIES OF THE PERM PROVINCE DURING THE PERIOD “PREPARATORY WORK FOR THE COMPULSORY EDUCATION INTROD... by S. V. Golikova

“…The choice of regional (provincial) scale of this process enables to consider features of the public educational policy at the subcountry level; the center-periphery approach to the discussed subject makes it possible to understand more deeply the essence and purposes of the major educational reform. …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…A novel approach is introduced by employing the Levenberg–Marquardt backpropagation (LMBP) algorithm for training the ANN. This model is designed to predict HPR based on critical operational parameters, including anode and cathode areas (mm2), cell voltage (V) and current (A), water flow rate (mL/min), power (W), and temperature (K). …”
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Applying Patient and Health Professional Preferences in Co-Designing a Digital Brief Intervention to Reduce the Risk of Prescription Opioid–Related Harm Among Patients With Chronic... by Rachel A Elphinston, Sue Pager, Farhad Fatehi, Michele Sterling, Kelly Brown, Paul Gray, Linda Hipper, Lauren Cahill, Maisa Ziadni, Peter Worthy, Jason P Connor

“…ObjectiveWe aimed to report on the second phase of the co-design of a digital brief intervention (BI) based on patient and health professional preferences. …”
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Latent Class Analysis of Gameplay Metrics from Youth Playing Robot ChampionsTM: Relations of Class Membership to Persistence and Intensity by Lawrence Scheier, William Hansen, Alex Stone, Jennifer Javornik

“…Findings: LCA identified four unique gameplay styles comprised of Fully Engaged, Engaged in Training, Engaged in Building, and Engaged in Driving. …”
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Psychoregulation of polymodal perception of older preschoolers with different speech characteristics by Irina Y. Murashova

“…To assess speech and characterize speech development, a methodology based on the guidelines of O.B. Inshakova (Inshakova, 2022) was used. …”
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Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea by Na Hyeon Yu, Daeun Shin, Ik Hee Ryu, Tae Keun Yoo, Kyungmin Koh

“…Various machine learning algorithms were trained by incorporating all features from the health check-up data in the development set. …”
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