Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta, Giuseppe Loseto

“…The framework prioritizes the customizability of AI algorithms and real-time information sharing between stakeholders. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

“…The performance of the model was evaluated in the test set. After automatic segmentation of the back contour, a back asymmetry index was calculated via computer vision algorithms to classify scoliosis into different severities. …”
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A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery by Su-Zhen Yang, Ming-Hui Peng, Quan Lin, Shi-Wei Guan, Kai-Lun Zhang, Hai-Bo Yu

“…Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking.ResultsA total of 335 patients underwent laparoscopic abdominal surgery. …”
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Feasibility of Digitally Identifying and Minimizing Stressors in Palliative Care Workplaces by Measuring Stress Continuously for Nurses Through Wearable Sensors (DiPa): Protocol fo... by Aaron Seehausen, Wencke Chodan, Florian Höpfner, Carolin Schneider, Sabine Felser, Hugo Murua Escobar, Mario Aehnelt, Christian Junghanss

“…The EMAs were prompted by scanning near-field communication (NFC) tags placed at different locations on the ward. The ongoing data analyses will be processed using computer algorithms partly programmed specifically for this study and partly drawn from existing libraries, such as toolboxes for neurophysiological signal processing for Python. …”
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An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children by Mengyu Duan, Zhimin Geng, Lichao Gao, Yonggen Zhao, Zheming Li, Lindong Chen, Pekka Kuosmanen, Guoqiang Qi, Fangqi Gong, Gang Yu

“…We collected demographic and laboratory data from 3650 patients (2299 with KD, 1351 with similar symptoms but different diseases) and employed 10 ML algorithms to construct the diagnostic model. …”
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Investigation of the physically unclonable function of a configurable ring oscillator by A. A. Ivaniuk

“…Additionally, the measured periods of CRO circuits clearly demonstrate their uniqueness, both when implemented on the same chip and across different chips. This serves as a foundation for exploring new methods and algorithms for calculating unique PUF responses.…”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…This study aims to rigorously quantify this temporal transferability gap for both pea yield and TR prediction. Four ML algorithms (RF, XGBoost, GPR, SVMr) were evaluated using Sentinel-2 and ERA5-Land data from 2018 to 2024 in northern Italy. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…MSCAM-GAN was tested on the Coleambally irrigated area and lower Gwydir catchment datasets and compared with classic spatiotemporal fusion algorithms. Ablation experiments were conducted to evaluate the effectiveness of the various submodules in MSCAM-GAN. …”
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Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…Additionally, the international reference ionosphere (IRI) models TEC maps are incorporated as constraints to form the Completion GAN Variational Autoencoder-label (CGVAE-label) model, enhancing large-scale data completion, robustness, and integration of prior knowledge. The study evaluates the models performance under varying conditions, including geomagnetic quiet/disturbed periods, high/low solar activity years, and different mask scales. …”
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Enhancing student reflections with natural language processing based scaffolding: A quasi-experimental study in a large lecture course by Muhsin Menekse, Alfa Satya Putra, Jiwon Kim, Ahmed Ashraf Butt, Mark A. McDaniel, Ido Davidesco, Michelle Cadieux, Joe Kim, Diane Litman

“…The app uses Natural Language Processing (NLP) algorithms to evaluate students' reflection quality and specificity using a 4-point scale, with 1 indicating shallow reflection and 4 indicating highly relevant or specific reflection. …”
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Ancient and contemporary shellfish cultivation practices bolster bivalve communities and diversity‐biomass relationships by Kieran D. Cox, Hailey L. Davies, Ben Millard‐Martin, Morgan Black, Margot Hessing‐Lewis, Nicole F. Smith, Francis Juanes, Sarah E. Dudas

“…We quantified the diversity, density and estimated biomass of the bivalve communities within 24 intertidal sites and evaluated the communities' association with the habitat's substrate composition, sediment characteristics and physical complexity. …”
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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

“…The concordance index (C-index) was used to evaluate the concordance of the prediction models. …”
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Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome by Yaxuan He, Zekai Chen, Zhaohui Tang, Yuexiang Qin, Fang Wang

“…Ten machine learning algorithms were evaluated using 10-fold cross-validation. …”
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Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, E... by Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle

“…This study used different machine learning classification algorithms to predict the features that cause virological failure in HIV-positive patients. …”
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The Relationship Between Stiff Knee Gait Runner’s Dystonia and Musculoskeletal Knee Pathology: A Case Series by Jared A. Stowers, Derek S. Day, Steven Jow, Sarah Heins, Euan Forrest, Yonathan M. Assefa, Paige M. Lind, Afreen Mushtaheed, Frances T. Sheehan, Katharine E. Alter

“…Methods: Eight RD cases, evaluated at the NIH Movement Disorders Clinic since 2018, were retrospectively reviewed. …”
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Simple energy detector for two-stage classification for antidrone systems by Zurovac Snežana, Petrović Nikola, Joksimović Vasilija, Pokrajac Ivan, Mikanović Darko, Sazdić-Jotić Boban

“…Furthermore, we conducted a comparative analysis of different deep learning algorithms at the outset of twostage classification, which is a potential basis for adopting this approach.…”
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Assessing marine ecosystem risks through unsupervised methods by Laura Pavirani, Pasquale Bove, Gianpaolo Coro

“…Traditional approaches, such as Ecosystem Models (EMs) and Marine Spatial Planning (MSP), often rely on expert knowledge, which introduces subjective assumptions. This study evaluates six unsupervised methods — four clustering algorithms (Multi K-means, Fuzzy C-means, X-means, and DBSCAN) and two machine-learning models (an Artificial Neural Network, ANN, and a Variational Autoencoder, VAE) — to assess marine ecosystem risk in the Mediterranean Sea automatically, using open-access data from 2017 to 2021. …”
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…Second, we noticed inconsistent accuracy scores across different pretrained models, prompting the need for interpretable explanations to validate these results. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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Higher-order aberrations and spherical aberration in various age groups after LASIK and cataract surgery by Robert Edward T. Ang, Patricia L. Lee, Ryan S. Torres, Michelle Marie Q. Araneta, Emerson M. Cruz, Maria Socorro M. Torno

“…Treated patients were classified into 2 groups according to treatment administered: eyes that underwent different algorithms of laser in situ keratomileusis (Lasik) demonstrating quantifiable differences in corneal aberrations, and eyes that underwent uncomplicated phacoemulsification with in-the-bag implantation of aspheric neutral EnVista MX60 (Bausch & Lomb, USA), negative aspheric Tecnis ZCB00 (Johnson and Johnson, USA), or spherical Akreos Adapt (Bausch & Lomb, USA) intraocular lens (IOL) showing differences in internal spherical aberrations. …”
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