Usability and accessibility in mHealth stroke apps: An empirical assessment by Pontus Svensson, Shuanglan Lin, Leonardo Horn Iwaya

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Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features by Shenghua Xu, Zhuolu Wang, Jiping Liu, Xinrui Ma, Tingting Zhou, Qing Tang

“…Taking Ya’an City in Sichuan Province as the study area, we compare the proposed DS-DRN method with three widely used deep learning methods: CNN, CPCNN-RF, and U-net. Evaluating model accuracy and performance, the DS-DRN method exhibits the highest prediction accuracy while also saving computational costs. …”
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Aides logicielles à la lecture de textes documentaires scientifiques by Jacques Crinon, Denis Legros, Brigitte Marin, Patrick Avel

“…A series of studies have allowed to elaborate a hypertext computer system that helps students to understand scientific texts and to evaluate the effect of two kinds of explanatory notes. …”
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Improving deep convolutional neural networks with mixed maxout units by Hui-zhen ZHAO, Fu-xian LIU, Long-yue LI, Chang LUO

“…The maxout units have the problem of not delivering non-max features， resulting in the insufficient of pooling operation over a subspace that is composed of several linear feature mappings,when they are applied in deep convolutional neural networks.The mixed maxout (mixout) units were proposed to deal with this constrain.Firstly,the exponential probability of the feature mappings getting from different linear transformations was computed.Then,the averaging of a subspace of different feature mappings by the exponential probability was computed.Finally,the output was randomly sampled from the max feature and the mean value by the Bernoulli distribution,leading to the better utilizing of model averaging ability of dropout.The simple models and network in network models was built to evaluate the performance of mixout units.The results show that mixout units based models have better performance.…”
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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

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Tuning into urban birdsong: enhancing nature connectedness with an AI-powered wearable by Zhuying Li, Si Cheng, Xiaoqing Sun, Xipei Ren, Yan Wang, Min-Ling Zhang

“…Additional questionnaires and semi-structured interviews were employed to evaluate the device’s usability and user experience. …”
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Improving Energy Efficiency and Autonomy Through the Development of a Hybrid Battery–Supercapacitor System in Electromobility by Michalakis Kotsias, Georgios Kontogogos, Spyridon Angelopoulos, Evangelos Hristoforou

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Digital Architectures for UWB Beamforming Using 2D IIR Spatio-Temporal Frequency-Planar Filters by Soumya Kondapalli, Arjuna Madanayake, Len Bruton

“…Frequency-planar beamforming enables highly-directional UWB RF beams at low computational complexity compared to digital phased-array feed techniques. …”
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HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images by Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Hyun-Soo Kang

“…Evaluated on benchmark datasets, HTTD achieves state-of-the-art results, with precision rates of 96.98% on ICDAR-2019 cTDaR, 96.43% on TNCR, and 93.14% on TabRecSet. …”
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Biosynthesis and characterization of copper oxide nanoparticles from Plumbago zeylanica leaf extract for antibacterial and antioxidant activities by Hailemichael Tegenu Gebrie, Melesse Ababay Assege, Desta Shumuye Meshesha, Birhanu Ayalew Tebeje, Getaneh Worku Moges, Ayalew Temesgen Wodajo, Gizachew Mulugeta Manahelohe, Aderaw Anteneh Belew

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Thermal Noise‐Induced Phase Transition in Multi‐Domain Hf‐Based Antiferroelectric Material: Fatigue and Endurance Performance by Sheng Luo, Zijie Zheng, Zuopu Zhou, Xiao Gong, Gengchiau Liang

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The fine art of fine-tuning: A structured review of advanced LLM fine-tuning techniques by Samar Pratap, Alston Richard Aranha, Divyanshu Kumar, Gautam Malhotra, Anantharaman Palacode Narayana Iyer, Shylaja S.S.

“…We collated the results of various techniques on common benchmarks and also evaluated their performance on different datasets and base models.…”
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Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies by Abishek Sankaranarayanan, Georgii Khachaturov, Kimberly S. Smythe, Shachi Mittal

“…Pre-trained StarDist model is recommended in case of limited computational resources, providing ~12x run time improvement with CPU compute and ~4x improvement with the GPU compute over Mesmer, but it struggles in dense nuclear regions.…”
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Damage and failure modeling of lotus-type porous material subjected to low-cycle fatigue by J. Kramberger, K. Sterkuš, S. Glodež

“…Porous materials exhibit some unique features which are useful for a number of various applications. This paper evaluates a numerical approach for determining of damage initiation and evolution of lotus-type porous material with computational simulations, where the considered computational models have different pore topology patterns. …”
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Epiregulon: Single-cell transcription factor activity inference to predict drug response and drivers of cell states by Tomasz Włodarczyk, Aaron Lun, Diana Wu, Minyi Shi, Xiaofen Ye, Shreya Menon, Shushan Toneyan, Kerstin Seidel, Liang Wang, Jenille Tan, Shang-Yang Chen, Timothy Keyes, Aleksander Chlebowski, Adrian Waddell, Wei Zhou, Yangmeng Wang, Qiuyue Yuan, Yu Guo, Liang-Fu Chen, Bence Daniel, Antonina Hafner, Meng He, Alejandro Chibly, Yuxin Liang, Zhana Duren, Ciara Metcalfe, Marc Hafner, Christian W. Siebel, M. Ryan Corces, Robert Yauch, Shiqi Xie, Xiaosai Yao

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Revival of Muslin by Phuti Karpas plant identification with convolution neural network by Redwan Ahmed Rizvee, Omar Farrok, Mahamudul Hasan, Faisal Farhan, Md Hafanul Islam, Md Khalid Hasan, Abidur Rahman, Maheen Islam, Md Sawkat Ali, Taskeed Jabid, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam

“…A unique dataset of 2354 leaf images was curated, with two main classes: Phuti Karpas and Non Phuti Karpas, the latter including 14 other plant types to enhance model robustness. Each model was evaluated on metrics like accuracy, precision, recall, computational time, and memory efficiency. …”
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Benchmark dataset and deep learning method for global tropical cyclone forecasting by Cheng Huang, Pan Mu, Jinglin Zhang, Sixian Chan, Shiqi Zhang, Hanting Yan, Shengyong Chen, Cong Bai

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LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics by Yangen Zhan, Yongbing Zhang, Zheqi Hu, Yifeng Wang, Zirui Zhu, Sijing Du, Xiangming Yan, Xiu Li

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Improving Medical Image Segmentation Using Test-Time Augmentation with MedSAM by Wasfieh Nazzal, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio

“…The method generates several input variations during inference that are combined after, improving robustness and segmentation accuracy without requiring retraining. Evaluated across diverse computed tomography (CT) datasets, including Medical Segmentation Decathlon (MSD), KiTS, and COVID-19-20, the proposed method demonstrated consistent improvements in Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) metrics. …”
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A novel numerical implementation for solving time fractional telegraph differential equations having multiple space and time delays via Delannoy polynomial by Ömür Kıvanç Kürkçü

“…The method is evaluated under some valuable error tests in thenumerical applications. …”
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